Frugal Incremental Generative Modeling using Variational Autoencoders | Paper Citation Record

Observation f21f0e69-2c2d-4a0d-af6a-b33bdc35fb77 · outbound

This paper cites Catastrophic interference in connectionist networks: The sequential learning problem.

Frugal Incremental Generative Modeling using Variational Autoencoders Catastrophic interference in connectionist networks: The sequential learning problem

Reference 1

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Observation 029d6aed-0cdb-4e3d-9237-797120beb5f7 · outbound

This paper cites Lifelong robot learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Lifelong robot learning

Reference 2

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Observation 71d2dae3-8404-40b9-a59e-41e78ff4ce01 · outbound

This paper cites Child: A first step towards continual learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Child: A first step towards continual learning

Reference 3

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Observation 35ef20ac-48a7-4a53-9f84-bb6949c02b0c · outbound

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

Frugal Incremental Generative Modeling using Variational Autoencoders Continual lifelong learning with neural networks: A review

Reference 4

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This paper cites Attention is all you need.

Frugal Incremental Generative Modeling using Variational Autoencoders Attention is all you need

Reference 5

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Observation dda5864b-7551-4e8a-a398-4e8f9b64f02c · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Frugal Incremental Generative Modeling using Variational Autoencoders An image is worth 16x16 words: Transformers for image recognition at scale

Reference 6

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Observation 6173f2d9-0df2-4567-abb8-d093b738f0e5 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Frugal Incremental Generative Modeling using Variational Autoencoders Learning transferable visual models from natural language supervision

Reference 7

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Observation 2608c5d4-29c5-4a62-adb5-d5f38560eac5 · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.

Frugal Incremental Generative Modeling using Variational Autoencoders Scaling up visual and vision-language representation learning with noisy text supervision

Reference 8

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Observation 8d6a7660-a7ee-4b9b-ad80-c7a1328eed98 · outbound

This paper cites CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet.

Frugal Incremental Generative Modeling using Variational Autoencoders CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracy with ViT-B and ViT-L on ImageNet

Reference 9

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Observation 7fd2f8f7-2388-4242-b16e-1c7f49268c50 · outbound

This paper cites Improving clip fine-tuning performance.

Frugal Incremental Generative Modeling using Variational Autoencoders Improving clip fine-tuning performance

Reference 10

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Observation 3dfaa028-6fbf-45dd-855c-837481dc4484 · outbound

This paper cites Preventing zero-shot transfer degradation in continual learning of vision-language models.

Frugal Incremental Generative Modeling using Variational Autoencoders Preventing zero-shot transfer degradation in continual learning of vision-language models

Reference 11

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Observation 27572c2f-80f9-4ba9-8154-b3f07956da7c · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

Frugal Incremental Generative Modeling using Variational Autoencoders LoRA: Low-rank adaptation of large language models

Reference 12

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Observation e657c9c2-11f9-4241-8650-72a824941f3d · outbound

This paper cites Parameter-efficient transfer learning for nlp.

Frugal Incremental Generative Modeling using Variational Autoencoders Parameter-efficient transfer learning for nlp

Reference 13

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Observation 49045ae0-7c67-4eeb-bb0a-e694f3d4bf4f · outbound

This paper cites Learning to prompt for vision-language models.

Frugal Incremental Generative Modeling using Variational Autoencoders Learning to prompt for vision-language models

Reference 14

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Observation 82113461-4f96-46ef-8095-064ba1f56a3b · outbound

This paper cites Conditional prompt learning for vision- language models.

Frugal Incremental Generative Modeling using Variational Autoencoders Conditional prompt learning for vision- language models

Reference 15

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Observation 1dd4d506-cc4f-4809-ab39-33e2f9d34116 · outbound

This paper cites Attriclip: A non-incremental learner for incremental knowledge learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Attriclip: A non-incremental learner for incremental knowledge learning

Reference 16

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Observation a201de89-a384-47aa-b495-17de2b0442fe · outbound

This paper cites Clip with generative latent replay: a strong baseline for incremental learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Clip with generative latent replay: a strong baseline for incremental learning

Reference 17

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Observation 50bdafa4-54fc-49f7-ba01-2c40b7115ba6 · outbound

This paper cites Semantic residual prompts for continual learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Semantic residual prompts for continual learning

Reference 18

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Observation 4a6e0e39-453e-4a21-ad25-3ecc982a2fe4 · outbound

This paper cites Fetril: Feature translation for exemplar-free class-incremental learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Fetril: Feature translation for exemplar-free class-incremental learning

Reference 19

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Observation ea887598-c6b9-4cb3-be9a-dce480b0e9cb · outbound

This paper cites Class-incremental learning via dual augmentation.

Frugal Incremental Generative Modeling using Variational Autoencoders Class-incremental learning via dual augmentation

Reference 20

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Observation 4acb628e-2a4e-47b0-b5a6-ddb5f179fa2a · outbound

This paper cites Prototype augmentation and self- supervision for incremental learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Prototype augmentation and self- supervision for incremental learning

Reference 21

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Observation 71bd33a6-1de1-46cf-a83d-bbc84aa2ea3b · outbound

This paper cites Diffusion Model Meets Non-Exemplar Class-Incremental Learning and Beyond.

Frugal Incremental Generative Modeling using Variational Autoencoders Diffusion Model Meets Non-Exemplar Class-Incremental Learning and Beyond

Reference 22

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Observation 9c42cbe5-399c-4948-8fd1-50a8b814dfdf · outbound

This paper cites Generative feature replay for class-incremental learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Generative feature replay for class-incremental learning

Reference 23

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Observation 3c9c0d1e-bc76-483e-b89a-4e87ab7df154 · outbound

This paper cites Brain-inspired replay for continual learning with artificial neural networks.

Frugal Incremental Generative Modeling using Variational Autoencoders Brain-inspired replay for continual learning with artificial neural networks

Reference 24

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Observation 1bd32914-ca59-4a42-a803-3c388f6c9239 · outbound

This paper cites Task-agnostic Continual Learning with Hybrid Probabilistic Models.

Frugal Incremental Generative Modeling using Variational Autoencoders Task-agnostic Continual Learning with Hybrid Probabilistic Models

Reference 25

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Observation c1eabf96-d92d-4a83-a5b1-f9e4d65cce4d · outbound

This paper cites Auto-Encoding Variational Bayes.

Frugal Incremental Generative Modeling using Variational Autoencoders Auto-Encoding Variational Bayes

Reference 26

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Observation 86f341eb-d1e3-49db-bf78-9f0b6e7ea769 · outbound

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

Frugal Incremental Generative Modeling using Variational Autoencoders Training networks in null space of feature covariance for continual learning

Reference 27

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Observation a6cd054f-0d23-489f-bc53-4af29a9d65d0 · outbound

This paper cites Packnet: Adding multiple tasks to a single network by iterative pruning.

Frugal Incremental Generative Modeling using Variational Autoencoders Packnet: Adding multiple tasks to a single network by iterative pruning

Reference 28

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Observation 9feec1a5-bd43-4314-853f-7d65e381161e · outbound

This paper cites Overcoming catastrophic forgetting with hard attention to the task.

Frugal Incremental Generative Modeling using Variational Autoencoders Overcoming catastrophic forgetting with hard attention to the task

Reference 29

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This paper cites Lifelong Learning with Dynamically Expandable Networks.

Frugal Incremental Generative Modeling using Variational Autoencoders Lifelong Learning with Dynamically Expandable Networks

Reference 30

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This paper cites Progressive Neural Networks.

Frugal Incremental Generative Modeling using Variational Autoencoders Progressive Neural Networks

Reference 31

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This paper cites Continual learning through synaptic intelligence.

Frugal Incremental Generative Modeling using Variational Autoencoders Continual learning through synaptic intelligence

Reference 32

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Observation b1602c79-e815-45c2-b380-6230edca8fad · outbound

This paper cites Overcoming catastrophic forgetting in neural networks.

Frugal Incremental Generative Modeling using Variational Autoencoders Overcoming catastrophic forgetting in neural networks

Reference 33

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Observation 5d929050-0c76-429f-aa87-053542aaa7c9 · outbound

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

Frugal Incremental Generative Modeling using Variational Autoencoders Memory aware synapses: Learning what (not) to forget

Reference 34

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Observation 3c5f42c9-98f4-4e48-a422-e4b64073a681 · outbound

This paper cites Overcoming catas- trophic forgetting by incremental moment matching.

Frugal Incremental Generative Modeling using Variational Autoencoders Overcoming catas- trophic forgetting by incremental moment matching

Reference 35

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Observation d87fcebb-9fe6-4981-a85b-2d96c7fb2746 · outbound

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

Frugal Incremental Generative Modeling using Variational Autoencoders Continual learning of context-dependent processing in neural networks

Reference 36

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Observation 11b795f3-88ef-4062-8ad0-d0079bf847e0 · outbound

This paper cites Gradient Projection Memory for Continual Learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Gradient Projection Memory for Continual Learning

Reference 37

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Observation 75289c6a-d431-4030-9bf2-e0c340f6b33d · outbound

This paper cites FFNB: Forgetting-Free Neural Blocks for Deep Continual Visual Learning.

Frugal Incremental Generative Modeling using Variational Autoencoders FFNB: Forgetting-Free Neural Blocks for Deep Continual Visual Learning

Reference 38

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Observation 341722cd-584f-4dfc-ae0a-77a1bad9f3ea · outbound

This paper cites Gradient based sample selection for online continual learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Gradient based sample selection for online continual learning

Reference 39

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Observation 37ada724-1e0c-4bfb-9a05-cc78684f4a9c · outbound

This paper cites Selective experience replay for lifelong learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Selective experience replay for lifelong learning

Reference 40

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Observation e6205d80-ca20-4139-adab-85b00694e151 · outbound

This paper cites Riemannian walk for incremental learning: Understanding forgetting and intransigence.

Frugal Incremental Generative Modeling using Variational Autoencoders Riemannian walk for incremental learning: Understanding forgetting and intransigence

Reference 41

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Observation 260215e8-d574-4958-bc89-dd7b6af3625b · outbound

This paper cites Using hindsight to anchor past knowledge in continual learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Using hindsight to anchor past knowledge in continual learning

Reference 42

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Observation 8371a731-fa01-47a5-8f9f-27454d14d0aa · outbound

This paper cites Lifelong gan: Continual learning for conditional image generation.

Frugal Incremental Generative Modeling using Variational Autoencoders Lifelong gan: Continual learning for conditional image generation

Reference 43

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Observation a7fd2380-4fe0-419a-8b52-3f5a91756460 · outbound

This paper cites Variational Continual Learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Variational Continual Learning

Reference 44

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This paper cites Continual learning with deep generative replay.

Frugal Incremental Generative Modeling using Variational Autoencoders Continual learning with deep generative replay

Reference 45

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This paper cites Generative adversarial networks.Communications of the ACM, 63(11):139– 144, 2020.

Frugal Incremental Generative Modeling using Variational Autoencoders Generative adversarial networks.Communications of the ACM, 63(11):139– 144, 2020

Reference 46

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This paper cites Generative replay with feedback connections as a general strategy for continual learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Generative replay with feedback connections as a general strategy for continual learning

Reference 47

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Observation ac864da0-4a55-48f2-8cb2-2ea802c88edf · outbound

This paper cites Normalizing flows: An introduction and review of current methods.

Frugal Incremental Generative Modeling using Variational Autoencoders Normalizing flows: An introduction and review of current methods

Reference 48

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Observation d45310a9-6e0b-485c-a0c1-365db5ce3306 · outbound

This paper cites Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning

Reference 49

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Observation 70e8eced-f12a-4acb-96fc-07b0fccaedbb · outbound

This paper cites Ddgr: Continual learning with deep diffusion-based generative replay.

Frugal Incremental Generative Modeling using Variational Autoencoders Ddgr: Continual learning with deep diffusion-based generative replay

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:09.889665Z

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Observation fc79f17f-5c48-46f5-bb5e-e3d05b3ad9ea · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Frugal Incremental Generative Modeling using Variational Autoencoders Deep unsupervised learning using nonequilibrium thermodynamics

Reference 51

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Observation c99b4215-974b-4d70-b510-6510e3c079e5 · outbound

This paper cites Unrolled Generative Adversarial Networks.

Frugal Incremental Generative Modeling using Variational Autoencoders Unrolled Generative Adversarial Networks

Reference 52

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Observation 4a0e5a8d-be10-48ec-b3b1-02edb3f279b7 · outbound

This paper cites Normalizing Flows for Probabilistic Modeling and Inference.

Frugal Incremental Generative Modeling using Variational Autoencoders Normalizing Flows for Probabilistic Modeling and Inference

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:09.702838Z

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Observation 79952061-bef6-4e54-99f7-039323fac1c3 · outbound

This paper cites One-for-More: Continual Diffusion Model for Anomaly Detection.

Frugal Incremental Generative Modeling using Variational Autoencoders One-for-More: Continual Diffusion Model for Anomaly Detection

Reference 54

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Observation 34c3400e-0852-42f0-8862-60e5b8672592 · outbound

This paper cites Incremental Learning of Structured Memory via Closed-Loop Transcription.

Frugal Incremental Generative Modeling using Variational Autoencoders Incremental Learning of Structured Memory via Closed-Loop Transcription

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:17:06.296452Z

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Observation e572ffde-08fd-4a14-b2fd-e82d930814b2 · outbound

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

Frugal Incremental Generative Modeling using Variational Autoencoders Dualprompt: Complementary prompting for rehearsal-free continual learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:09.527941Z

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Observation 1b4a1d20-fed7-4ca5-ab0c-dbfb17d03455 · outbound

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

Frugal Incremental Generative Modeling using Variational Autoencoders Coda-prompt: Continual decomposed attention- based prompting for rehearsal-free continual learning

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:09.283649Z

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Observation 258c5ad9-bfe1-4f12-9ef3-6ba33418d6ba · outbound

This paper cites Visual prompt tuning.

Frugal Incremental Generative Modeling using Variational Autoencoders Visual prompt tuning

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:09.043460Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 70b0ce14-66ec-4272-8278-3f657915a7a5 · outbound

This paper cites Visual Prompt Tuning in Null Space for Continual Learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Visual Prompt Tuning in Null Space for Continual Learning

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:04.922920Z

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Observation 25c4a8b0-2942-4e3d-ad43-d0fdaf94913a · outbound

This paper cites Prompt gradient projection for continual learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Prompt gradient projection for continual learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:08.855776Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 49cac82d-0b0c-4443-9592-e337b064dab0 · outbound

This paper cites Consistent prompting for rehearsal-free continual learning.

Frugal Incremental Generative Modeling using Variational Autoencoders Consistent prompting for rehearsal-free continual learning

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:08.610452Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f6af2463-c670-403e-95f5-030ff39fc6ca · outbound

This paper cites Understanding Diffusion Models: A Unified Perspective.

Frugal Incremental Generative Modeling using Variational Autoencoders Understanding Diffusion Models: A Unified Perspective

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:05.150237Z

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This paper cites Learning conditionally untangled latent spaces using fixed point iteration.

Frugal Incremental Generative Modeling using Variational Autoencoders Learning conditionally untangled latent spaces using fixed point iteration

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:08.420790Z

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Observation fd56d17e-db00-4b6c-ad1d-8cd7548f18ae · outbound

This paper cites an unresolved cited work.

Frugal Incremental Generative Modeling using Variational Autoencoders Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:17:08.135596Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 03078c1c-246d-46b2-8e79-37c25d2b1b8c · outbound

This paper cites juill 2011.

Frugal Incremental Generative Modeling using Variational Autoencoders juill 2011

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:07.967161Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 79ed40a9-fabe-4c0b-a8df-6a97a890ca54 · outbound

This paper cites 3d object representations for fine-grained categorization.

Frugal Incremental Generative Modeling using Variational Autoencoders 3d object representations for fine-grained categorization

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:07.804001Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f8648d63-15c2-4077-93e6-9c682575d4ce · outbound

This paper cites The many faces of robustness: A critical analysis of out-of-distribution generalization.

Frugal Incremental Generative Modeling using Variational Autoencoders The many faces of robustness: A critical analysis of out-of-distribution generalization

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:05.492745Z

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Unavailable: canonical work link unavailable.

Observation 056b5b89-25a5-47ac-96bf-55dfbd118c9f · outbound

This paper cites Learning multiple layers of features from tiny images.(2009), 2009.

Frugal Incremental Generative Modeling using Variational Autoencoders Learning multiple layers of features from tiny images.(2009), 2009

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:05.574533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4fccc14b-b988-430e-8039-6d8de4b2ab5a · outbound

This paper cites Slca: Slow learner with classifier alignment for continual learning on a pre-trained model.

Frugal Incremental Generative Modeling using Variational Autoencoders Slca: Slow learner with classifier alignment for continual learning on a pre-trained model

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:05.636146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa28e101-b878-41ee-8233-abd9fc5a5b1e · outbound

This paper cites SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-training.

Frugal Incremental Generative Modeling using Variational Autoencoders SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-training

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:05.721636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de508825-8934-43e0-bc11-08c7982fe3ba · outbound

This paper cites Boosting continual learning of vision-language models via mixture-of-experts adapters.

Frugal Incremental Generative Modeling using Variational Autoencoders Boosting continual learning of vision-language models via mixture-of-experts adapters

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:07.636023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7d8dcc4f-e34d-4174-a849-aeba3984c644 · outbound

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

Frugal Incremental Generative Modeling using Variational Autoencoders A comprehensive survey of continual learning: theory, method and application

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:05.859378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3e3edfb-33b7-452f-9336-9f49610b4757 · outbound

This paper cites Deep residual learning for image recognition.

Frugal Incremental Generative Modeling using Variational Autoencoders Deep residual learning for image recognition

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:05.922792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67e0a078-2ba2-4371-a5af-3dc1ba87e812 · outbound

This paper cites icarl: Incremental classifier and representation learning.

Frugal Incremental Generative Modeling using Variational Autoencoders icarl: Incremental classifier and representation learning

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T13:17:06.002466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f69c4f8-2226-4d7a-98d3-c1687b99bf02 · outbound

This paper cites proportion.

Frugal Incremental Generative Modeling using Variational Autoencoders proportion

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:17:07.437984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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