CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection | Paper Citation Record

Observation 31e90525-ab19-4c77-8f08-d83b50a150b9 · outbound

This paper cites Dimension- ality reduction via multiple neighborhood-aware nonlinear collaborative analysis for hyperspectral image classification,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Dimension- ality reduction via multiple neighborhood-aware nonlinear collaborative analysis for hyperspectral image classification,

Reference 1

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Observation 2923ad00-d0be-4b46-b368-66d4b8ac9a7e · outbound

This paper cites Hyperspectral anomaly detection for spectral anomaly targets via spatial and spectral constraints,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Hyperspectral anomaly detection for spectral anomaly targets via spatial and spectral constraints,

Reference 2

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Observation b0cacf46-cb23-4417-a9a3-3591ef81e23f · outbound

This paper cites One-step detection paradigm for hyperspectral anomaly detection via spectral deviation relationship learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection One-step detection paradigm for hyperspectral anomaly detection via spectral deviation relationship learning,

Reference 3

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Observation 7393858c-7d8d-4200-8786-979460341def · outbound

This paper cites Interactive spectral- spatial transformer for hyperspectral image classification,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Interactive spectral- spatial transformer for hyperspectral image classification,

Reference 4

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Observation 88f7e4de-ef30-4d84-a63a-002793fdd8c8 · outbound

This paper cites Continual lifelong learning with neural networks: A review,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Continual lifelong learning with neural networks: A review,

Reference 5

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Observation 58631beb-b30b-4ab6-aa01-f8e35849467c · outbound

This paper cites A continual learning survey: Defying forgetting in classification tasks,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection A continual learning survey: Defying forgetting in classification tasks,

Reference 6

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Observation 0d08a903-b5d5-4923-a588-ecd33e6563ed · outbound

This paper cites Class-incremental learning: Survey and performance evaluation on image classification,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Class-incremental learning: Survey and performance evaluation on image classification,

Reference 7

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Observation 95c72763-5269-4940-9d7b-6a570f24f83e · outbound

This paper cites Catastrophic interference in connec- tionist networks: The sequential learning problem,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Catastrophic interference in connec- tionist networks: The sequential learning problem,

Reference 8

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Observation 2ea6294f-24bf-47b5-bf06-81ad651bc836 · outbound

This paper cites Loss of plasticity in deep continual learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Loss of plasticity in deep continual learning,

Reference 9

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Observation 95d654b5-aeed-44f0-a377-98c9e0395cad · outbound

This paper cites A comprehensive survey of continual learning: Theory, method and application,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection A comprehensive survey of continual learning: Theory, method and application,

Reference 10

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Observation 4cd5fc99-8aab-42bd-b8a5-438a6c99f32f · outbound

This paper cites Esdb: Expand the shrinking decision boundary via one-to-many information matching for continual learning with small memory,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Esdb: Expand the shrinking decision boundary via one-to-many information matching for continual learning with small memory,

Reference 11

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Observation 65085d6f-e19b-45b5-ab0a-98af41062e9c · outbound

This paper cites Incorporating neuro-inspired adaptability for continual learning in artificial intelligence,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Incorporating neuro-inspired adaptability for continual learning in artificial intelligence,

Reference 12

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Observation eece43af-b74b-4008-b773-124f46cc9efd · outbound

This paper cites AFEC: Active forgetting of negative transfer in continual learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection AFEC: Active forgetting of negative transfer in continual learning,

Reference 13

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Observation a8498143-ffdf-4a1c-9fc6-17b11142011a · outbound

This paper cites Synaptic plasticity as bayesian inference,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Synaptic plasticity as bayesian inference,

Reference 14

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Observation eb90acf0-457b-4236-b031-1eb9744db522 · outbound

This paper cites Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemma,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.903606Z

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Observation 479d7e8a-7c6e-4870-9652-0e8feb082947 · outbound

This paper cites Kaizen: Practical self-supervised continual learning with continual fine-tuning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Kaizen: Practical self-supervised continual learning with continual fine-tuning,

Reference 18

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Observation 854c9e11-8cae-41c4-bdab-2aa3fd48c5e5 · outbound

This paper cites Integrating Present and Past in Unsupervised Continual Learning.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Integrating Present and Past in Unsupervised Continual Learning

Reference 19

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Observation 9914ed44-3c9d-438f-b089-4dc5dd9284cb · outbound

This paper cites Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning

Reference 20

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Observation 98857201-9e67-450b-91fb-d02deb290942 · outbound

This paper cites Continual learning of medical image classification based on feature replay,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Continual learning of medical image classification based on feature replay,

Reference 21

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Observation 06890cc9-ed02-4741-8a36-9538f504601b · outbound

This paper cites Con- trastive continuity on augmentation stability rehearsal for continual self- supervised learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Con- trastive continuity on augmentation stability rehearsal for continual self- supervised learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.859412Z

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Observation 0fe8daaa-b2ca-40f8-8b59-73ec0a9b3c89 · outbound

This paper cites BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning

Reference 23

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Observation f72c9a8e-e6f0-4016-9d0c-92d6fac5e9a6 · outbound

This paper cites Exemplar-based contin- ual learning via contrastive learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Exemplar-based contin- ual learning via contrastive learning,

Reference 24

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Observation 0c9772ee-86fb-4931-9b37-3b1777e1e9fb · outbound

This paper cites Relational experience replay: Continual learning by adap- tively tuning task-wise relationship,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Relational experience replay: Continual learning by adap- tively tuning task-wise relationship,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.837029Z

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Observation 704582b0-8f87-4169-a457-3c36e7fae9b7 · outbound

This paper cites Class-incremental learning via deep model consolidation,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Class-incremental learning via deep model consolidation,

Reference 26

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Observation 092eef2f-beeb-452e-8056-bcd56d266fcb · outbound

This paper cites Class similarity weighted knowledge distillation for continual semantic segmentation,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Class similarity weighted knowledge distillation for continual semantic segmentation,

Reference 27

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Observation 42090886-ba53-45cc-8d34-1d8e19d31bfb · outbound

This paper cites A contrastive continual learning for the classification of remote sensing imagery,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection A contrastive continual learning for the classification of remote sensing imagery,

Reference 28

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Observation 883201a0-2bca-46d8-be30-cf21f7b16947 · outbound

This paper cites Continual barlow twins: Continual self- supervised learning for remote sensing semantic segmentation,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Continual barlow twins: Continual self- supervised learning for remote sensing semantic segmentation,

Reference 29

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Observation 8625a3b5-e5a5-476d-a950-0834093f242b · outbound

This paper cites Achieving a better stability-plasticity trade-off via auxiliary networks in continual learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Achieving a better stability-plasticity trade-off via auxiliary networks in continual learning,

Reference 30

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Observation b19d4dc5-550e-4a45-9218-12bd7f3bba71 · outbound

This paper cites Piggyback: Adapting a single network to multiple tasks by learning to mask weights,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Piggyback: Adapting a single network to multiple tasks by learning to mask weights,

Reference 31

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Observation b098cabe-a573-4b0c-ada9-f2d92784a558 · outbound

This paper cites Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Learn to grow: A continual structure learning framework for overcoming catastrophic forgetting,

Reference 32

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Observation f2ff1ba9-f9ce-4c71-b2e4-324c88ad60bb · outbound

This paper cites Meta-Attention for ViT- backed continual learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Meta-Attention for ViT- backed continual learning,

Reference 33

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Observation 7270f7e3-b5f0-4841-b596-a14bf40493cd · outbound

This paper cites Adversarial continual learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Adversarial continual learning,

Reference 34

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Observation 0190e73f-21f2-49a5-8770-ddc155c5922a · outbound

This paper cites Design of distributed rule-based models in the presence of large data,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Design of distributed rule-based models in the presence of large data,

Reference 35

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Observation 3d32b3fa-1ebc-4b08-bd32-96ead23efefb · outbound

This paper cites Balancing stability and plas- ticity through advanced null space in continual learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Balancing stability and plas- ticity through advanced null space in continual learning,

Reference 37

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Observation d6e94a56-d8cb-4286-b801-2bd086bc8807 · outbound

This paper cites Embracing change: Continual learning in deep neural networks,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Embracing change: Continual learning in deep neural networks,

Reference 38

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This paper cites Anti- retroactive interference for lifelong learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Anti- retroactive interference for lifelong learning,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.696181Z

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Observation 6c0d9096-003b-42f0-8641-68313cf0e4be · outbound

This paper cites Optimizing reusable knowledge for continual learning via metalearning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Optimizing reusable knowledge for continual learning via metalearning,

Reference 40

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Observation 709410a6-72e5-467e-afbe-7cb745568109 · outbound

This paper cites Towards better plasticity-stability trade-off in incremental learning: A simple linear connector,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Towards better plasticity-stability trade-off in incremental learning: A simple linear connector,

Reference 41

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Observation 58b5a8ff-5a33-40f8-b737-71718be915a7 · outbound

This paper cites Training networks in null space of feature covariance for continual learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Training networks in null space of feature covariance for continual learning,

Reference 42

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Observation 7737c3a1-5a8f-4b74-b04b-5dd9914e4f3a · outbound

This paper cites The challenges of con- tinuous self-supervised learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection The challenges of con- tinuous self-supervised learning,

Reference 43

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Observation e9343bed-f589-4bc1-af5d-46678388012d · outbound

This paper cites Self-supervised models are continual learners,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Self-supervised models are continual learners,

Reference 44

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Observation 30997929-0f9e-4a69-b321-b165c3c31352 · outbound

This paper cites Learning to prompt for continual learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Learning to prompt for continual learning,

Reference 45

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Observation db49a4e2-2ee8-466e-bda4-e53373364374 · outbound

This paper cites Coda-prompt: Contin- ual decomposed attention-based prompting for rehearsal-free continual learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Coda-prompt: Contin- ual decomposed attention-based prompting for rehearsal-free continual learning,

Reference 46

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Observation f493b7b4-a507-4017-9607-07f364cf26e2 · outbound

This paper cites Dualprompt: Complementary prompting for rehearsal-free continual learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Dualprompt: Complementary prompting for rehearsal-free continual learning,

Reference 47

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Observation a094c4a5-f9e8-4021-9346-59ed84da4b5b · outbound

This paper cites S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection S-prompts learning with pre-trained transformers: An occam’s razor for domain incremental learning,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.621880Z

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Observation 8db1e93c-fcf4-4f89-9281-fb1729dc30c4 · outbound

This paper cites Passnet: A spatial–spectral feature extraction network with patch attention module for hyperspectral image classification,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Passnet: A spatial–spectral feature extraction network with patch attention module for hyperspectral image classification,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.610186Z

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Observation 2a5554c5-4b84-47be-a551-255e09cacea0 · outbound

This paper cites RX anomaly detector with rectified background,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection RX anomaly detector with rectified background,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.598991Z

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Observation c0004986-f717-40c2-896a-e859459e8e74 · outbound

This paper cites Kernel RX-algorithm: A nonlinear anomaly detector for hyperspectral imagery,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Kernel RX-algorithm: A nonlinear anomaly detector for hyperspectral imagery,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.588613Z

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Observation 2f49c849-19b9-4179-973b-bbfac2d4a92e · outbound

This paper cites A locally adaptive background density estimator: An evolution for RX-based anomaly detectors,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection A locally adaptive background density estimator: An evolution for RX-based anomaly detectors,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.578302Z

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Observation 9403a4af-9e0f-4848-9296-2677728c60a6 · outbound

This paper cites Fractional fourier transform- based tensor RX for hyperspectral anomaly detection,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Fractional fourier transform- based tensor RX for hyperspectral anomaly detection,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.567066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7d96a83a-a5b8-4903-a57d-45d9f1155b69 · outbound

This paper cites Recursive RX with extended multi-attribute profiles for hyperspectral anomaly detection,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Recursive RX with extended multi-attribute profiles for hyperspectral anomaly detection,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.555450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 530f3a08-1e3d-4fad-8ed8-e67e23e3005e · outbound

This paper cites Adaptive reference-related graph embedding for hyperspectral anomaly detection,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Adaptive reference-related graph embedding for hyperspectral anomaly detection,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.543660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 24c3801b-aaa7-40f8-9a78-00ee60018944 · outbound

This paper cites Collaborative representation for hyperspectral anomaly detection,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Collaborative representation for hyperspectral anomaly detection,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.533475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8f244e48-107f-4970-a3f3-137a97b52fc7 · outbound

This paper cites Anomaly detection in hyperspectral images based on low-rank and sparse representation,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Anomaly detection in hyperspectral images based on low-rank and sparse representation,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.522986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a9567205-df65-45ba-9fe7-dbfa4d02c83c · outbound

This paper cites Effective anomaly space for hyperspectral anomaly detec- tion,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Effective anomaly space for hyperspectral anomaly detec- tion,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.512535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f39a19b4-19e2-4251-b048-d13de8456fa7 · outbound

This paper cites Hyperspectral anomaly detection based on chessboard topology,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Hyperspectral anomaly detection based on chessboard topology,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.501323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8d94369a-0d83-446b-9391-42d737c0d1e2 · outbound

This paper cites Learning tensor low-rank representation for hyperspectral anomaly detection,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Learning tensor low-rank representation for hyperspectral anomaly detection,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.489790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ffb7d871-0dcf-465d-87f2-166b608f26a3 · outbound

This paper cites Enhanced total variation reg- ularized representation model with endmember background dictionary for hyperspectral anomaly detection,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Enhanced total variation reg- ularized representation model with endmember background dictionary for hyperspectral anomaly detection,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.478941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 29056af0-b63f-455b-b348-d9fddc7e204c · outbound

This paper cites FusAtNet: Dual attention based spectrospatial multimodal fusion network for hyperspec- tral and lidar classification,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection FusAtNet: Dual attention based spectrospatial multimodal fusion network for hyperspec- tral and lidar classification,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.468337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4c823ed1-047e-4750-a722-5d940fdcde1f · outbound

This paper cites Enhanced autoencoders with attention-embedded degradation learning for unsupervised hyperspectral image super-resolution,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Enhanced autoencoders with attention-embedded degradation learning for unsupervised hyperspectral image super-resolution,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.456940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bb20ed66-e128-49e6-b060-a38e57a0c56b · outbound

This paper cites Background-guided deformable convolutional autoencoder for hyperspectral anomaly detection,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Background-guided deformable convolutional autoencoder for hyperspectral anomaly detection,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.446429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f9a969c5-8d04-4c4a-ad98-8040abf8050d · outbound

This paper cites Auto-AD: Autonomous hyperspectral anomaly detection network based on fully convolutional autoencoder,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Auto-AD: Autonomous hyperspectral anomaly detection network based on fully convolutional autoencoder,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T20:51:53.105273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ca3f0c2-1706-422b-a409-3e91bb39a3aa · outbound

This paper cites GAN-based hyperspectral anomaly detection,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection GAN-based hyperspectral anomaly detection,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.428084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cd24d739-c2b5-4715-830e-aad74dcd7f4f · outbound

This paper cites Dual- channel capsule generation adversarial network for hyperspectral image classification,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Dual- channel capsule generation adversarial network for hyperspectral image classification,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.417056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b0373955-5647-4b39-909e-1247503205ac · outbound

This paper cites Semisupervised spectral learning with generative adversarial network for hyperspectral anomaly detection,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Semisupervised spectral learning with generative adversarial network for hyperspectral anomaly detection,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T20:51:53.116351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 966765ef-1c37-4a77-a29a-049d42d20f52 · outbound

This paper cites Convolutional transformer- inspired autoencoder for hyperspectral anomaly detection,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Convolutional transformer- inspired autoencoder for hyperspectral anomaly detection,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.397094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e233ead4-36c1-4fdc-9b3e-8155ce96b18f · outbound

This paper cites Hyperspectral anomaly detection based on variational background inference and generative adversarial network,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Hyperspectral anomaly detection based on variational background inference and generative adversarial network,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.384397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d952e3bf-f886-4b07-98a0-e12bb24cde5c · outbound

This paper cites Variational Continual Learning.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Variational Continual Learning

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T20:51:53.126436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfb07aaf-3101-4686-96ef-dcab91c380c5 · outbound

This paper cites Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.372784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d4af4cd4-5eef-4cf1-b430-01bff3ed2094 · outbound

This paper cites Autoencoders, minimum description length and helmholtz free energy,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Autoencoders, minimum description length and helmholtz free energy,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.361328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6b5a4454-bea3-4b1c-a08e-75b36c57cf50 · outbound

This paper cites Tanet: An unsupervised two-stream autoencoder network for hyperspectral unmixing,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Tanet: An unsupervised two-stream autoencoder network for hyperspectral unmixing,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.350011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0f34af75-066b-49d6-aad4-41bc6a79814f · outbound

This paper cites Target detection with unconstrained linear mixture model and hierarchical denoising autoencoder in hyperspectral imagery,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Target detection with unconstrained linear mixture model and hierarchical denoising autoencoder in hyperspectral imagery,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.338805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 160ccb3a-061c-4ada-b79d-94d11996a753 · outbound

This paper cites Maximum likelihood from incomplete data via the em algorithm,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Maximum likelihood from incomplete data via the em algorithm,

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-15T20:51:53.143726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 510f03d1-5af6-49d8-bcb6-f565cd906819 · outbound

This paper cites Hyperspectral anomaly detection with attribute and edge-preserving filters,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Hyperspectral anomaly detection with attribute and edge-preserving filters,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-15T20:51:53.147055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8af5090-f77a-4d0e-a2c1-592d523573cc · outbound

This paper cites You only train once: Learning a general anomaly enhancement network with random masks for hyperspectral anomaly detection,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection You only train once: Learning a general anomaly enhancement network with random masks for hyperspectral anomaly detection,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.314415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 33dd8d47-e9d4-4f38-b115-4012e88d4a79 · outbound

This paper cites Memory aware synapses: Learning what (not) to forget,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Memory aware synapses: Learning what (not) to forget,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.303345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 91551a67-219c-4d1d-ac77-256754e8760f · outbound

This paper cites Overcoming catastrophic forgetting in neural networks,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Overcoming catastrophic forgetting in neural networks,

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-15T20:51:53.156712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7eee6310-5e19-4699-ae42-c0ccbe04dba7 · outbound

This paper cites Continual learning of context- dependent processing in neural networks,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Continual learning of context- dependent processing in neural networks,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-15T20:51:53.160796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6746e258-686c-49ad-a33f-24a9f47052ba · outbound

This paper cites CL-CaGAN: Capsule differential adversarial continual learning for cross-domain hyperspectral anomaly detection,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection CL-CaGAN: Capsule differential adversarial continual learning for cross-domain hyperspectral anomaly detection,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.285091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4e872c65-b8ec-44f6-b1fc-898abfc82eb3 · outbound

This paper cites Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-15T20:51:53.168284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b97437f-4c6e-4d6c-8c12-6fd69474d360 · outbound

This paper cites A coherent interpretation of auc as a measure of aggregated classification performance,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection A coherent interpretation of auc as a measure of aggregated classification performance,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.268004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 370731f0-5146-46b2-8907-a2a63ed472ec · outbound

This paper cites Component decomposition analysis for hyperspectral anomaly detection,.

CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection Component decomposition analysis for hyperspectral anomaly detection,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:51:53.256881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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