Quantifying the Variability Collapse of Neural Networks

6 June 2023

Papers citing "Quantifying the Variability Collapse of Neural Networks"

9 / 9 papers shown

Title
Balancing Two Classifiers via A Simplex ETF Structure for Model Calibration Jiani Ni He Zhao Jintong Gao Dandan Guo H. Zha 24 0 0 14 Apr 2025
MaxSup: Overcoming Representation Collapse in Label Smoothing Yuxuan Zhou Heng Li Zhi-Qi Cheng Xudong Yan Mario Fritz M. Keuper 36 0 0 18 Feb 2025
Relaxed Contrastive Learning for Federated Learning Seonguk Seo Jinkyu Kim Geeho Kim Bohyung Han FedML 34 8 0 10 Jan 2024
Perturbation Analysis of Neural Collapse Tom Tirer Haoxiang Huang Jonathan Niles-Weed AAML 27 23 0 29 Oct 2022
Improving Self-Supervised Learning by Characterizing Idealized Representations Yann Dubois Tatsunori Hashimoto Stefano Ermon Percy Liang SSL 59 40 0 13 Sep 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 258 7,337 0 11 Nov 2021
Rethinking Supervised Pre-training for Better Downstream Transferring Yutong Feng Jianwen Jiang Mingqian Tang R. L. Jin Yue Gao SSL 40 38 0 12 Oct 2021
An Unconstrained Layer-Peeled Perspective on Neural Collapse Wenlong Ji Yiping Lu Yiliang Zhang Zhun Deng Weijie J. Su 122 83 0 06 Oct 2021
Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training Cong Fang Hangfeng He Qi Long Weijie J. Su FAtt 114 162 0 29 Jan 2021