Image de couverture de livre

Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteur principal:	Bao, Han
Format:	Preprint
Publié:	2023
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis
par: Ishikawa, Satoki, et autres
Publié: (2024)

Preventing Collapse in Contrastive Learning with Orthonormal Prototypes (CLOP)
par: Li, Huanran, et autres
Publié: (2024)

Explaining and Preventing Alignment Collapse in Iterative RLHF
par: Gauthier, Etienne, et autres
Publié: (2026)

Golden Ratio Weighting Prevents Model Collapse
par: He, Hengzhi, et autres
Publié: (2025)

TQL: Scaling Q-Functions with Transformers by Preventing Attention Collapse
par: Dong, Perry, et autres
Publié: (2026)

Preventing Model Collapse via Contraction-Conditioned Neural Filters
par: Han, Zongjian, et autres
Publié: (2025)

Why Prototypes Collapse: Diagnosing and Preventing Partial Collapse in Prototypical Self-Supervised Learning
par: Arteaga, Gabriel Y., et autres
Publié: (2025)

Neural Collapse Dynamics: Depth, Activation, Regularisation, and Feature Norm Threshold
par: Rupa, Anamika Paul
Publié: (2026)

Preventing Dimensional Collapse in Self-Supervised Learning via Orthogonality Regularization
par: He, Junlin, et autres
Publié: (2024)

Collapse-Proof Non-Contrastive Self-Supervised Learning
par: Sansone, Emanuele, et autres
Publié: (2024)

Preventing Model Collapse in Gaussian Process Latent Variable Models
par: Li, Ying, et autres
Publié: (2024)

Preventing Representational Rank Collapse in MPNNs by Splitting the Computational Graph
par: Roth, Andreas, et autres
Publié: (2024)

Sequential Subspace Noise Injection Prevents Accuracy Collapse in Certified Unlearning
par: Dolgova, Polina, et autres
Publié: (2026)

Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularization
par: He, Junlin, et autres
Publié: (2024)

Preventing Curriculum Collapse in Self-Evolving Reasoning Systems
par: Mishra, Vaibhav
Publié: (2026)

Towards Understanding Neural Collapse: The Effects of Batch Normalization and Weight Decay
par: Pan, Leyan, et autres
Publié: (2023)

A Theoretical Framework for Preventing Class Collapse in Supervised Contrastive Learning
par: Lee, Chungpa, et autres
Publié: (2025)

LaDi-RL: Latent Diffusion Reasoning Prevents Entropy Collapse in Reinforcement Learning
par: Kang, Haoqiang, et autres
Publié: (2026)

Neural Collapse Beyond the Unconstrained Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime
par: Wu, Diyuan, et autres
Publié: (2025)

Hard Regularization to Prevent Deep Online Clustering Collapse without Data Augmentation
par: Mahon, Louis, et autres
Publié: (2023)

Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse
par: Alman, Josh, et autres
Publié: (2025)

Anchored Alignment: Preventing Positional Collapse in Multimodal Recommender Systems
par: Jeong, Yonghun, et autres
Publié: (2026)

IConE: Batch Independent Collapse Prevention for Self-Supervised Representation Learning
par: Almpanakis, Konstantinos, et autres
Publié: (2026)

Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs
par: Scholkemper, Michael, et autres
Publié: (2024)

Understanding and Preventing Entropy Collapse in RLVR with On-Policy Entropy Flow Optimization
par: Xu, Huimin, et autres
Publié: (2026)

CURE: Critical-Token-Guided Re-Concatenation for Entropy-Collapse Prevention
par: Li, Qingbin, et autres
Publié: (2025)

Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning
par: Arefin, Md Rifat, et autres
Publié: (2024)

Neural Collapse for Cross-entropy Class-Imbalanced Learning with Unconstrained ReLU Feature Model
par: Dang, Hien, et autres
Publié: (2024)

Preventing Model Collapse Under Overparametrization: Optimal Mixing Ratios for Interpolation Learning and Ridge Regression
par: Garg, Anvit, et autres
Publié: (2025)

Domain Feature Collapse: Implications for Out-of-Distribution Detection and Solutions
par: Yang, Hong, et autres
Publié: (2025)

Normalization in Proportional Feature Spaces
par: Benatti, Alexandre, et autres
Publié: (2024)

Non-Stationary Online Structured Prediction with Surrogate Losses
par: Sakaue, Shinsaku, et autres
Publié: (2025)

A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training Loops
par: Fu, Shi, et autres
Publié: (2025)

Beyond Unconstrained Features: Neural Collapse for Shallow Neural Networks with General Data
par: Hong, Wanli, et autres
Publié: (2024)

Silent Collapse in Recursive Learning Systems
par: Zhang, Zhipeng
Publié: (2026)

The Non-Local Model Merging Problem: Permutation Symmetries and Variance Collapse
par: Sharma, Ekansh, et autres
Publié: (2024)

CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments
par: Marasco, Isabella, et autres
Publié: (2026)

Non-Normal Diffusion Models
par: Li, Henry
Publié: (2024)

Neural Collapse in Cumulative Link Models for Ordinal Regression: An Analysis with Unconstrained Feature Model
par: Ma, Chuang, et autres
Publié: (2025)

Age-Normalized HRV Features for Non-Invasive Glucose Prediction: A Pilot Sleep-Aware Machine Learning Study
par: Azam, Md Basit, et autres
Publié: (2025)