Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition | Paper Citation Record

Observation 827be298-ff66-4d6a-b195-0b27b822afcc · outbound

This paper cites Rainbow memory: Continual learn- ing with a memory of diverse samples.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Rainbow memory: Continual learn- ing with a memory of diverse samples

Reference 1

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This paper cites Towards open set deep networks.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Towards open set deep networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:35.210516Z

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Observation 66ee2229-6355-46b1-991b-e54e11e8169d · outbound

This paper cites Best fitting hyperplanes for classification.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Best fitting hyperplanes for classification

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:35.186077Z

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Observation 87a720e8-3028-4d2b-9c2f-2a09c6f5ebd2 · outbound

This paper cites Riemannian walk for incremen- tal learning: Understanding forgetting and intransigence.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Riemannian walk for incremen- tal learning: Understanding forgetting and intransigence

Reference 4

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Observation df633b0e-a5cd-4070-993c-3bb1d70c9f5c · outbound

This paper cites Adversarial reciprocal points learning for open set recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11):8065–8081, 2021.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Adversarial reciprocal points learning for open set recognition.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11):8065–8081, 2021

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:35.142765Z

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Observation 687d05d3-9c32-4020-9a18-c151947ae3ab · outbound

This paper cites Reducing network agnostophobia.Advances in Neural In- formation Processing Systems, 31, 2018.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Reducing network agnostophobia.Advances in Neural In- formation Processing Systems, 31, 2018

Reference 6

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Observation da15bd8b-94b2-4ddd-8931-5a77531de59b · outbound

This paper cites Podnet: Pooled outputs dis- tillation for small-tasks incremental learning.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Podnet: Pooled outputs dis- tillation for small-tasks incremental learning

Reference 7

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Observation b02e4e68-cb41-4900-ab57-edb729fd0e13 · outbound

This paper cites Reason and discov- ery: A new paradigm for open set recognition.IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 2025.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Reason and discov- ery: A new paradigm for open set recognition.IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 2025

Reference 8

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Observation 76cf5773-f4ad-4a75-a5c3-8757cd48eda7 · outbound

This paper cites Logit proto- type learning with active multimodal representation for ro- bust open-set recognition.Science China Information Sci- ences, 67(6):162204, 2024.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Logit proto- type learning with active multimodal representation for ro- bust open-set recognition.Science China Information Sci- ences, 67(6):162204, 2024

Reference 9

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Observation 63b71ebf-20d0-4e74-a215-c4b9fbee02f3 · outbound

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Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Unresolved cited work

Reference 10

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Observation 01f1532d-6703-41f0-a2c9-fe8360d87312 · outbound

This paper cites Generative OpenMax for Multi-Class Open Set Classification.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Generative OpenMax for Multi-Class Open Set Classification

Reference 11

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Observation a8221e46-cb83-4845-8152-33af3b4e74b1 · outbound

This paper cites Toward open-set face recognition.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Toward open-set face recognition

Reference 12

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Observation 94e397f2-c39f-4605-a1b4-0b9e30f081ea · outbound

This paper cites Dyson: Dynamic feature space self- organization for online task-free class incremental learning.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Dyson: Dynamic feature space self- organization for online task-free class incremental learning

Reference 13

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Observation a125fb37-4f3c-43b9-8488-fdbea68682dc · outbound

This paper cites Learning a unified classifier incrementally via rebalancing.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Learning a unified classifier incrementally via rebalancing

Reference 14

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This paper cites Distilling causal effect of data in class- incremental learning.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Distilling causal effect of data in class- incremental learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.982847Z

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Observation 8cf71bff-a506-4773-a5c2-c88f0c9402d1 · outbound

This paper cites Teacher–explorer– student learning: A novel learning method for open set recognition.IEEE Transactions on Neural Networks and Learning Systems, 2023.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Teacher–explorer– student learning: A novel learning method for open set recognition.IEEE Transactions on Neural Networks and Learning Systems, 2023

Reference 16

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Observation 5bf1620d-0371-4bfe-b239-f408930e6c9b · outbound

This paper cites Ib-drr- incremental learning with information-back discrete repre- sentation replay.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Ib-drr- incremental learning with information-back discrete repre- sentation replay

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.940776Z

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Observation ed84ae3b-7781-4a21-85c7-070202062b87 · outbound

This paper cites Alleviating repre- sentational shift for continual fine-tuning.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Alleviating repre- sentational shift for continual fine-tuning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.917650Z

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Observation bed6794e-4286-455c-b16a-7b0748972181 · outbound

This paper cites Hard negative mix- ing for contrastive learning.Advances in neural information processing systems, 33:21798–21809, 2020.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Hard negative mix- ing for contrastive learning.Advances in neural information processing systems, 33:21798–21809, 2020

Reference 19

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Observation 1227e89e-d7f8-48a5-b5bd-c421337f7b4a · outbound

This paper cites Overcoming catastrophic forgetting in neu- ral networks.Proceedings of the national academy of sci- ences, 114(13):3521–3526, 2017.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Overcoming catastrophic forgetting in neu- ral networks.Proceedings of the national academy of sci- ences, 114(13):3521–3526, 2017

Reference 20

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This paper cites Opengan: Open-set recog- nition via open data generation.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Opengan: Open-set recog- nition via open data generation

Reference 21

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Observation a03bc37d-5121-4e95-ae1e-bc3a68c2b3b9 · outbound

This paper cites Learning without forgetting.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Learning without forgetting

Reference 22

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Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Unresolved cited work

Reference 23

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Observation 973e07d4-5527-4671-946c-3937feaa332c · outbound

This paper cites Pmal: Open set recognition via robust prototype mining.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Pmal: Open set recognition via robust prototype mining

Reference 24

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Observation 87612ef1-5a8e-4d71-8252-c31a88411b57 · outbound

This paper cites Class anchor clustering: A loss for distance-based open set recognition.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Class anchor clustering: A loss for distance-based open set recognition

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.771620Z

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Observation 87fc0dbc-b1a4-445a-a1b0-8a5484feff0f · outbound

This paper cites Open set learning with counterfactual images.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Open set learning with counterfactual images

Reference 26

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Observation d41f2d08-5d1a-4cb2-b102-a7fcb0833984 · outbound

This paper cites Prevalence of neural collapse during the terminal phase of deep learning training.Proceedings of the National Academy of Sciences, 117(40):24652–24663, 2020.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Prevalence of neural collapse during the terminal phase of deep learning training.Proceedings of the National Academy of Sciences, 117(40):24652–24663, 2020

Reference 27

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Observation 023ff0d5-d63b-472f-84a8-c9659365e27a · outbound

This paper cites Generative-discriminative feature representations for open-set recognition.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Generative-discriminative feature representations for open-set recognition

Reference 28

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Observation 2acb1e40-7a59-4dce-bfa1-279f2a47476f · outbound

This paper cites Class-incremental learn- ing with pre-allocated fixed classifiers.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Class-incremental learn- ing with pre-allocated fixed classifiers

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.692544Z

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Observation 27560486-da2f-42d3-ad5d-b5a88b6559ff · outbound

This paper cites icarl: Incremental classifier and representation learning.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition icarl: Incremental classifier and representation learning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.672388Z

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Observation 41f1fad0-f2fa-4ebf-ae0d-0ebd65b94eee · outbound

This paper cites Toward open set recogni- tion.IEEE transactions on pattern analysis and machine intelligence, 35(7):1757–1772, 2012.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Toward open set recogni- tion.IEEE transactions on pattern analysis and machine intelligence, 35(7):1757–1772, 2012

Reference 31

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Observation 7a772f6e-8e00-4128-b696-8f4411351615 · outbound

This paper cites Prob- ability models for open set recognition.IEEE transactions on pattern analysis and machine intelligence, 36(11):2317– 2324, 2014.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Prob- ability models for open set recognition.IEEE transactions on pattern analysis and machine intelligence, 36(11):2317– 2324, 2014

Reference 32

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Observation a6f36f4b-0e68-4dd3-b93b-0c9d7a235753 · outbound

This paper cites Mimicking the oracle: An initial phase decorrelation ap- proach for class incremental learning.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Mimicking the oracle: An initial phase decorrelation ap- proach for class incremental learning

Reference 33

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This paper cites Always be dreaming: A new approach for data-free class-incremental learning.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Always be dreaming: A new approach for data-free class-incremental learning

Reference 34

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Observation 772b5bde-76a1-4882-9f4c-ffd0208489d4 · outbound

This paper cites Exemplar-free class incremen- tal learning via discriminative and comparable parallel one- class classifiers.Pattern Recognition, 140:109561, 2023.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Exemplar-free class incremen- tal learning via discriminative and comparable parallel one- class classifiers.Pattern Recognition, 140:109561, 2023

Reference 35

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Observation 539fd8c7-7723-462e-ae24-6bc8c309a7c7 · outbound

This paper cites Conditional gaussian distribution learning for open set recognition.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Conditional gaussian distribution learning for open set recognition

Reference 36

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Observation 2bb391fb-46f4-4f25-9107-14f9cc4bccd4 · outbound

This paper cites Prototype Fission: Closing Set for Robust Open-set Semi-supervised Learning.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Prototype Fission: Closing Set for Robust Open-set Semi-supervised Learning

Reference 37

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Observation 16bed20e-1d18-46df-a79d-84ef994d987d · outbound

This paper cites Open-set recognition: A good closed-set classifier is all you need? 2021.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Open-set recognition: A good closed-set classifier is all you need? 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.505215Z

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Observation 6a7886cd-173f-44b6-86b7-b3c17de347fe · outbound

This paper cites Ex- ploring diverse representations for open set recognition.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Ex- ploring diverse representations for open set recognition

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.482550Z

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Observation 3c32c334-58f5-4aa4-9bf7-dfe811e49913 · outbound

This paper cites Large scale incre- mental learning.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Large scale incre- mental learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.461488Z

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Observation 2694e653-2007-4b8a-a873-e5ba653ada33 · outbound

This paper cites Robust classification with convolutional proto- type learning.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Robust classification with convolutional proto- type learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.436389Z

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Observation 8afd3d46-2dbf-4f66-ba1a-69c7b01c5cfc · outbound

This paper cites Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning

Reference 42

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Observation 2b6383b4-3612-4a3b-8964-1bc8710de5ae · outbound

This paper cites Adaptive deep models for incremental learning: Considering capacity scalability and sustainability.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Adaptive deep models for incremental learning: Considering capacity scalability and sustainability

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.414145Z

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Observation 2845cd6e-fe5f-49e2-8bd3-0b56927dc034 · outbound

This paper cites Classification- reconstruction learning for open-set recognition.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Classification- reconstruction learning for open-set recognition

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.388830Z

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Observation f86cd7ff-227d-4280-acaf-228c117c33f6 · outbound

This paper cites Deep-learning- based open set fault diagnosis by extreme value theory.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Deep-learning- based open set fault diagnosis by extreme value theory

Reference 45

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Observation 9afeecce-524b-4f4e-aac9-0a921bcd9eaa · outbound

This paper cites Contin- ual learning through synaptic intelligence.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Contin- ual learning through synaptic intelligence

Reference 46

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Observation 2ea549f1-e921-4326-bbec-23b4888faf14 · outbound

This paper cites Maintaining discrimination and fairness in class incremental learning.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Maintaining discrimination and fairness in class incremental learning

Reference 47

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Observation cc6cf9f6-663a-4c0b-aef4-82657bef23ae · outbound

This paper cites Memory-efficient class-incremental learning for image classification.IEEE Transactions on Neural Networks and Learning Systems, 33(10):5966–5977, 2021.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Memory-efficient class-incremental learning for image classification.IEEE Transactions on Neural Networks and Learning Systems, 33(10):5966–5977, 2021

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.282121Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dc61326f-0de8-4cc2-85e9-950e6ca5a100 · outbound

This paper cites Class-incremental learning: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Class-incremental learning: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.256183Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8a326d1b-5fab-4588-b853-329f321a9ae3 · outbound

This paper cites A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T16:20:34.061557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 819b7f25-0821-4a63-9393-257cac624863 · outbound

This paper cites Learning placeholders for open-set recognition.

Evolving from Unknown to Known: Retentive Angular Representation Learning for Incremental Open Set Recognition Learning placeholders for open-set recognition

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:20:34.232462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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