How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self
Distillation Networks

How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks

3 July 2024

Preetum Nakkiran

Joshua Susskind

Papers citing "How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks"

6 / 6 papers shown

Title
Wearable Accelerometer Foundation Models for Health via Knowledge Distillation Salar Abbaspourazad Anshuman Mishra Joseph D. Futoma Andrew C. Miller Ian Shapiro 83 0 0 15 Dec 2024
Revisiting Feature Prediction for Learning Visual Representations from Video Adrien Bardes Q. Garrido Jean Ponce Xinlei Chen Michael G. Rabbat Yann LeCun Mahmoud Assran Nicolas Ballas MDE VLM 67 70 0 15 Feb 2024
WERank: Towards Rank Degradation Prevention for Self-Supervised Learning Using Weight Regularization Ali Saheb Pasand Reza Moravej Mahdi Biparva Ali Ghodsi 19 2 0 14 Feb 2024
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
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 283 5,723 0 29 Apr 2021
Understanding self-supervised Learning Dynamics without Contrastive Pairs Yuandong Tian Xinlei Chen Surya Ganguli SSL 130 258 0 12 Feb 2021