Paper Citation Record · LEDGER

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning

As of 21 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2501.15454.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.15454 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:20:00.187060Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T12:42:05.261084Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T12:42:07.186021Z

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 0725dfde-f2ab-41ce-ab60-473631796518 · outbound

This paper cites Conditional channel gated networks for task-aware continual learning.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Conditional channel gated networks for task-aware continual learning

Reference 1

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Observation 310f6927-c8e0-41cb-bf99-93ad4c8d4f5a · outbound

This paper cites Expanding hyperspherical space for few-shot class- incremental learning.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Expanding hyperspherical space for few-shot class- incremental learning

Reference 3

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Observation af8ae5d7-a4a2-4ed4-a227-a84d2401be64 · outbound

This paper cites Deep residual learning for image recognition.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Deep residual learning for image recognition

Reference 6

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Observation 1adbc9e5-3046-469c-b77f-0981cf826a68 · outbound

This paper cites Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning

Reference 13

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Observation 8a3e619a-6aff-4461-bd9b-ad7a7cd41690 · outbound

This paper cites Class incremental learning via likelihood ratio based task prediction.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Class incremental learning via likelihood ratio based task prediction

Reference 14

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Observation 3b48e870-587c-4734-a93b-cfd745204762 · outbound

This paper cites Task-adaptive saliency guidance for exemplar-free class incremental learning.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Task-adaptive saliency guidance for exemplar-free class incremental learning

Reference 15

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Observation 720e2786-e425-4756-b083-5f0baccabe6e · outbound

This paper cites Learning with mixture of prototypes for out-of-distribution detection.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Learning with mixture of prototypes for out-of-distribution detection

Reference 16

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Observation 3456327d-25ad-4a3f-9cd5-bcd36c13d0e5 · outbound

This paper cites Bagdanov.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Bagdanov

Reference 17

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This paper cites Directional statistics.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Directional statistics

Reference 18

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Observation 7ff6e748-44ac-433c-90ce-ef72aba8c023 · outbound

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

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Catastrophic interference in connectionist networks: The sequential learning problem

Reference 19

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Observation 91b8a6ff-c150-423d-9310-fba21a8e9e78 · outbound

This paper cites How to exploit hyperspherical embeddings for out-of-distribution detection? In International Confer- ence on Learning Representations,.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning How to exploit hyperspherical embeddings for out-of-distribution detection? In International Confer- ence on Learning Representations,

Reference 21

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This paper cites Provable guarantees for understanding out-of-distribution detection.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Provable guarantees for understanding out-of-distribution detection

Reference 22

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Observation 06ac6411-5b2c-42cc-9652-e8147b5f40d8 · outbound

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

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Fetril: Feature translation for exemplar-free class-incremental learning

Reference 23

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Observation 007fc0b8-34f6-41ba-a726-ea4fc9847ac2 · outbound

This paper cites itaml: An incremental task-agnostic meta- learning approach.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning itaml: An incremental task-agnostic meta- learning approach

Reference 24

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Observation 76c51b2a-d0ef-43cb-af9f-d18ca8b2227a · outbound

This paper cites icarl: Incremental classifier and representation learning.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning icarl: Incremental classifier and representation learning

Reference 25

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Observation 0d15b565-704c-4054-861b-1ee8f0fea871 · outbound

This paper cites Imagenet large scale visual recogni- tion challenge.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Imagenet large scale visual recogni- tion challenge

Reference 26

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Observation 00ddf787-9b89-4d9f-a425-f5c07d2f4681 · outbound

This paper cites Divide and not for- get: Ensemble of selectively trained experts in continual learning.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Divide and not for- get: Ensemble of selectively trained experts in continual learning

Reference 27

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This paper cites Adaptive Hyperparameter Optimization for Continual Learning Scenarios.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Adaptive Hyperparameter Optimization for Continual Learning Scenarios

Reference 28

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

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Overcoming catastrophic forgetting with hard attention to the task

Reference 29

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Observation 3ff17b61-e2d6-456f-bcd6-8ee8e2905099 · outbound

This paper cites Continual learning with hypernetworks.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Continual learning with hypernetworks

Reference 30

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This paper cites BEEF: Bi-compatible class-incremental learning via energy-based expansion and fusion.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning BEEF: Bi-compatible class-incremental learning via energy-based expansion and fusion

Reference 32

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Observation 36eb8262-c799-42f4-bb10-b1fe74a3b0f0 · outbound

This paper cites DER: Dynamically expandable representation for class incremental learning.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning DER: Dynamically expandable representation for class incremental learning

Reference 33

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Observation 05b78036-0f16-448b-94fb-9c7920dde6e1 · outbound

This paper cites A model or 603 exemplars: Towards memory-efficient class-incremental learning.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning A model or 603 exemplars: Towards memory-efficient class-incremental learning

Reference 35

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Observation 0e4bf921-de25-4b69-8398-3558c704b0a5 · outbound

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

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Prototype augmentation and self-supervision for incremental learning

Reference 36

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Observation d2be0ca1-ba53-47f7-a631-84f3ff079437 · outbound

This paper cites Self-organizing pathway expansion for non-exemplar class-incremental learning.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Self-organizing pathway expansion for non-exemplar class-incremental learning

Reference 37

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Observation d5b77db5-9b23-48f2-a05d-5c21e899129d · outbound

This paper cites Acil: Analytic class-incremental learning with ab- solute memorization and privacy protection.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Acil: Analytic class-incremental learning with ab- solute memorization and privacy protection

Reference 38

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This paper cites 1: # Training Time 2: for t = 1, 2,.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning 1: # Training Time 2: for t = 1, 2,

Reference 39

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Observation 9f96261a-60b7-4f22-89f0-27cb445feca5 · outbound

This paper cites All methods employed a ResNet-18 network trained from scratch as the backbone, without leveraging any pre-trained models.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning All methods employed a ResNet-18 network trained from scratch as the backbone, without leveraging any pre-trained models

Reference 40

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Observation 95adc961-773c-44e6-9324-7caea14960ab · outbound

This paper cites Diffclass: Diffusion-based class incremental learning.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Diffclass: Diffusion-based class incremental learning

Reference 1989

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This paper cites For the CIFAR-100 and Tiny-ImageNet datasets, we train the backbone for 700 epochs using LARS [You et al., 2017] with an initial learning rate of 0.1 and a batch size of.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning For the CIFAR-100 and Tiny-ImageNet datasets, we train the backbone for 700 epochs using LARS [You et al., 2017] with an initial learning rate of 0.1 and a batch size of

Reference 2000

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This paper cites Tiny imagenet visual recognition challenge.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Tiny imagenet visual recognition challenge

Reference 2009

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This paper cites Learning without forgetting.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Learning without forgetting

Reference 2015

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This paper cites Posterior meta-replay for continual learning.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Posterior meta-replay for continual learning

Reference 2016

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This paper cites Learning multiple layers of features from tiny im- ages.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Learning multiple layers of features from tiny im- ages

Reference 2017

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This paper cites FOSTER: Feature boosting and compression for class-incremental learning.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning FOSTER: Feature boosting and compression for class-incremental learning

Reference 2019

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This paper cites Dark experience for general continual learning: a strong, sim- ple baseline.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Dark experience for general continual learning: a strong, sim- ple baseline

Reference 2020

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This paper cites Large Batch Training of Convolutional Networks.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Large Batch Training of Convolutional Networks

Reference 2021

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This paper cites Learnability and algorithm for con- tinual learning.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Learnability and algorithm for con- tinual learning

Reference 2022

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This paper cites Overcom- ing catastrophic forgetting in neural networks.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Overcom- ing catastrophic forgetting in neural networks

Reference 2023

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Observation 2d9ce1e0-6585-46f3-a237-46433372f4f2 · outbound

This paper cites Exemplar-free continual representation learning via learnable drift compensation.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Exemplar-free continual representation learning via learnable drift compensation

Reference 2024

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This paper cites Resurrecting old classes with new data for exemplar-free continual learn- ing.

On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning Resurrecting old classes with new data for exemplar-free continual learn- ing

Reference 2025

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Pith citing papers

Observation e71cfe4b-74cc-4493-adfb-266eae6af185 · inbound

Progressive Homeostatic and Plastic Prompt Tuning for Audio-Visual Multi-Task Incremental Learning cites this paper.

Progressive Homeostatic and Plastic Prompt Tuning for Audio-Visual Multi-Task Incremental Learning On the Discrimination and Consistency for Exemplar-Free Class Incremental Learning

Reference 59

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