Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse | Paper Citation Record

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This paper cites On the implicit geometry of cross- entropy parameterizations for label-imbalanced data.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse On the implicit geometry of cross- entropy parameterizations for label-imbalanced data

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This paper cites Dark experience for gen- eral continual learning: a strong, simple baseline.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Dark experience for gen- eral continual learning: a strong, simple baseline

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

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Riemannian walk for incremen- tal learning: Understanding forgetting and intransigence

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse MMDetection: Open MMLab Detection Toolbox and Benchmark

Reference 4

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This paper cites Adaptformer: Adapting vision transformers for scalable visual recogni- tion.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Adaptformer: Adapting vision transformers for scalable visual recogni- tion

Reference 5

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark

Reference 6

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Podnet: Pooled outputs dis- tillation for small-tasks incremental learning

Reference 8

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This paper cites Explor- ing deep neural networks via layer-peeled model: Minority collapse in imbalanced training.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Explor- ing deep neural networks via layer-peeled model: Minority collapse in imbalanced training

Reference 9

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This paper cites On the Role of Neural Collapse in Transfer Learning.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse On the Role of Neural Collapse in Transfer Learning

Reference 10

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This paper cites A unified continual learn- ing framework with general parameter-efficient tuning.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse A unified continual learn- ing framework with general parameter-efficient tuning

Reference 11

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This paper cites Fast r-cnn.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Fast r-cnn

Reference 12

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This paper cites Dissecting supervised contrastive learning.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Dissecting supervised contrastive learning

Reference 13

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This paper cites Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path

Reference 14

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This paper cites Deep residual learning for image recognition.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Deep residual learning for image recognition

Reference 15

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This paper cites Identity mappings in deep residual networks.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Identity mappings in deep residual networks

Reference 16

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This paper cites Con- strained few-shot class-incremental learning.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Con- strained few-shot class-incremental learning

Reference 17

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This paper cites Distilling the Knowledge in a Neural Network.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Distilling the Knowledge in a Neural Network

Reference 18

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This paper cites Learning a unified classifier incrementally via rebalancing.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Learning a unified classifier incrementally via rebalancing

Reference 19

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This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse LoRA: Low-Rank Adaptation of Large Language Models

Reference 20

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Squeeze-and-excitation net- works

Reference 21

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This paper cites Neural Collapse Inspired Federated Learning with Non-iid Data.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Neural Collapse Inspired Federated Learning with Non-iid Data

Reference 22

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This paper cites An Unconstrained Layer-Peeled Perspective on Neural Collapse.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse An Unconstrained Layer-Peeled Perspective on Neural Collapse

Reference 23

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This paper cites Vi- sual prompt tuning.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Vi- sual prompt tuning

Reference 24

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This paper cites Class- incremental learning by knowledge distillation with adaptive feature consolidation.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Class- incremental learning by knowledge distillation with adaptive feature consolidation

Reference 25

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This paper cites Overcoming catastrophic forgetting in neu- ral networks.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Overcoming catastrophic forgetting in neu- ral networks

Reference 26

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This paper cites Learning multiple layers of features from tiny images.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Learning multiple layers of features from tiny images

Reference 27

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Overcoming catastrophic forget- ting by incremental moment matching

Reference 28

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This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 29

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This paper cites Understanding and Improving Transfer Learning of Deep Models via Neural Collapse.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Understanding and Improving Transfer Learning of Deep Models via Neural Collapse

Reference 30

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This paper cites Learning without forgetting.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Learning without forgetting

Reference 31

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This paper cites Feature pyra- mid networks for object detection.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Feature pyra- mid networks for object detection

Reference 32

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This paper cites Model behavior preserving for class-incremental learning.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Model behavior preserving for class-incremental learning

Reference 33

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Neural collapse under cross-entropy loss

Reference 34

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This paper cites Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning

Reference 35

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This paper cites Neural col- lapse with unconstrained features.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Neural col- lapse with unconstrained features

Reference 36

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This paper cites Traces of class/cross-class structure per- vade deep learning spectra.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Traces of class/cross-class structure per- vade deep learning spectra

Reference 37

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This paper cites Prevalence of neural collapse during the terminal phase of deep learning training.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Prevalence of neural collapse during the terminal phase of deep learning training

Reference 38

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This paper cites Explicit regularization and implicit bias in deep network classifiers trained with the square loss.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Explicit regularization and implicit bias in deep network classifiers trained with the square loss

Reference 39

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This paper cites Gdumb: A simple approach that questions our progress in continual learning.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Gdumb: A simple approach that questions our progress in continual learning

Reference 40

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This paper cites icarl: Incremental classifier and representation learning.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse icarl: Incremental classifier and representation learning

Reference 41

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Progressive Neural Networks

Reference 42

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Overcoming catastrophic forgetting with hard attention to the task

Reference 43

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

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Coda-prompt: Contin- ual decomposed attention-based prompting for rehearsal-free continual learning

Reference 44

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This paper cites Imbalance trouble: Revisiting neural-collapse geometry.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Imbalance trouble: Revisiting neural-collapse geometry

Reference 45

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This paper cites Extended unconstrained fea- tures model for exploring deep neural collapse.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Extended unconstrained fea- tures model for exploring deep neural collapse

Reference 46

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This paper cites Visualizing data using t-sne.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Visualizing data using t-sne

Reference 47

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This paper cites The caltech-ucsd birds-200-2011 dataset.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse The caltech-ucsd birds-200-2011 dataset

Reference 48

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Triple-memory networks: A brain-inspired method for continual learning

Reference 49

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This paper cites Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Ordisco: Effective and efficient usage of incremental unlabeled data for semi-supervised continual learning

Reference 50

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This paper cites Coscl: Cooperation of small continual learners is stronger than a big one.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Coscl: Cooperation of small continual learners is stronger than a big one

Reference 51

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This paper cites Memory Replay with Data Compression for Continual Learning.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Memory Replay with Data Compression for Continual Learning

Reference 52

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This paper cites Hierarchical decomposition of prompt-based continual learning: Rethinking obscured sub- optimality.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Hierarchical decomposition of prompt-based continual learning: Rethinking obscured sub- optimality

Reference 53

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Observation b08df21c-8707-4c35-b996-5b8f0f760751 · outbound

This paper cites Towards understanding neural collapse in supervised contrastive learning with the information bottleneck method.

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Towards understanding neural collapse in supervised contrastive learning with the information bottleneck method

Reference 54

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

Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Dualprompt: Complementary prompting for rehearsal-free continual learning

Reference 55

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Learning to prompt for con- tinual learning

Reference 56

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse On the emergence of simplex symmetry in the final and penultimate layers of neural network classifiers

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Large scale incre- mental learning

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Neural collapse inspired attraction–repulsion-balanced loss for im- balanced learning

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Continual object detection via prototypical task correlation guided gating mechanism

Reference 60

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Separation and Concentration in Deep Networks

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Contin- ual learning through synaptic intelligence

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark

Reference 64

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Slca: Slow learner with classifier align- ment for continual learning on a pre-trained model

Reference 65

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Class-incremental learning via deep model consolidation

Reference 66

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Benchmarking omni-vision representation through the lens of visual realms

Reference 67

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Pyramid scene parsing network

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Understanding imbalanced semantic segmentation through neural collapse

Reference 69

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Revisiting class-incremental learning with pre- trained models: Generalizability and adaptivity are all you need

Reference 70

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Continual Learning with Pre-Trained Models: A Survey

Reference 71

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Expandable subspace ensemble for pre-trained model- based class-incremental learning

Reference 72

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse On the optimization landscape of neural col- lapse under mse loss: Global optimality with unconstrained features

Reference 73

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Melo: Low-rank adaptation is better than fine-tuning for medical image diagnosis

Reference 74

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse A geometric analysis of neu- ral collapse with unconstrained features

Reference 75

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Enhancing Pre-Trained Model-Based Class-Incremental Learning through Neural Collapse Each class includes 100 images

Reference 76

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Reference 77

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Reference 78

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