ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2302.12091
  4. Cited By
Random Teachers are Good Teachers

Random Teachers are Good Teachers

23 February 2023
Felix Sarnthein
Gregor Bachmann
Sotiris Anagnostidis
Thomas Hofmann
ArXivPDFHTML

Papers citing "Random Teachers are Good Teachers"

24 / 24 papers shown
Title
Provable Weak-to-Strong Generalization via Benign Overfitting
Provable Weak-to-Strong Generalization via Benign Overfitting
David X. Wu
A. Sahai
116
10
0
06 Oct 2024
PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis
PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis
Satoki Ishikawa
Makoto Yamada
Han Bao
Yuki Takezawa
118
0
0
23 May 2024
The Curious Case of Benign Memorization
The Curious Case of Benign Memorization
Sotiris Anagnostidis
Gregor Bachmann
Lorenzo Noci
Thomas Hofmann
AAML
85
10
0
25 Oct 2022
When Does Re-initialization Work?
When Does Re-initialization Work?
Sheheryar Zaidi
Tudor Berariu
Hyunjik Kim
J. Bornschein
Claudia Clopath
Yee Whye Teh
Razvan Pascanu
61
10
0
20 Jun 2022
Masked Siamese Networks for Label-Efficient Learning
Masked Siamese Networks for Label-Efficient Learning
Mahmoud Assran
Mathilde Caron
Ishan Misra
Piotr Bojanowski
Florian Bordes
Pascal Vincent
Armand Joulin
Michael G. Rabbat
Nicolas Ballas
SSL
78
318
0
14 Apr 2022
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified
  Understanding with Self-supervised Contrastive Learning
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning
Chaoning Zhang
Kang Zhang
Chenshuang Zhang
T. Pham
Chang D. Yoo
In So Kweon
SSL
52
71
0
30 Mar 2022
Does Knowledge Distillation Really Work?
Does Knowledge Distillation Really Work?
Samuel Stanton
Pavel Izmailov
Polina Kirichenko
Alexander A. Alemi
A. Wilson
FedML
57
220
0
10 Jun 2021
Knowledge distillation: A good teacher is patient and consistent
Knowledge distillation: A good teacher is patient and consistent
Lucas Beyer
Xiaohua Zhai
Amelie Royer
L. Markeeva
Rohan Anil
Alexander Kolesnikov
VLM
100
293
0
09 Jun 2021
Towards Understanding Knowledge Distillation
Towards Understanding Knowledge Distillation
Mary Phuong
Christoph H. Lampert
63
316
0
27 May 2021
VICReg: Variance-Invariance-Covariance Regularization for
  Self-Supervised Learning
VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
Adrien Bardes
Jean Ponce
Yann LeCun
SSL
DML
149
931
0
11 May 2021
What Are Bayesian Neural Network Posteriors Really Like?
What Are Bayesian Neural Network Posteriors Really Like?
Pavel Izmailov
Sharad Vikram
Matthew D. Hoffman
A. Wilson
UQCV
BDL
67
384
0
29 Apr 2021
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Jure Zbontar
Li Jing
Ishan Misra
Yann LeCun
Stéphane Deny
SSL
276
2,338
0
04 Mar 2021
Understanding self-supervised Learning Dynamics without Contrastive
  Pairs
Understanding self-supervised Learning Dynamics without Contrastive Pairs
Yuandong Tian
Xinlei Chen
Surya Ganguli
SSL
196
281
0
12 Feb 2021
Towards Understanding Ensemble, Knowledge Distillation and
  Self-Distillation in Deep Learning
Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning
Zeyuan Allen-Zhu
Yuanzhi Li
FedML
114
370
0
17 Dec 2020
Exploring Simple Siamese Representation Learning
Exploring Simple Siamese Representation Learning
Xinlei Chen
Kaiming He
SSL
245
4,036
0
20 Nov 2020
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data
  Efficiency and Imperfect Teacher
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher
Guangda Ji
Zhanxing Zhu
71
44
0
20 Oct 2020
Bootstrap your own latent: A new approach to self-supervised Learning
Bootstrap your own latent: A new approach to self-supervised Learning
Jean-Bastien Grill
Florian Strub
Florent Altché
Corentin Tallec
Pierre Harvey Richemond
...
M. G. Azar
Bilal Piot
Koray Kavukcuoglu
Rémi Munos
Michal Valko
SSL
333
6,773
0
13 Jun 2020
Be Your Own Teacher: Improve the Performance of Convolutional Neural
  Networks via Self Distillation
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation
Linfeng Zhang
Jiebo Song
Anni Gao
Jingwei Chen
Chenglong Bao
Kaisheng Ma
FedML
60
857
0
17 May 2019
Asymmetric Valleys: Beyond Sharp and Flat Local Minima
Asymmetric Valleys: Beyond Sharp and Flat Local Minima
Haowei He
Gao Huang
Yang Yuan
ODL
MLT
61
150
0
02 Feb 2019
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Jonathan Frankle
Michael Carbin
204
3,457
0
09 Mar 2018
Essentially No Barriers in Neural Network Energy Landscape
Essentially No Barriers in Neural Network Energy Landscape
Felix Dräxler
K. Veschgini
M. Salmhofer
Fred Hamprecht
MoMe
105
432
0
02 Mar 2018
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
Florian Schroff
Dmitry Kalenichenko
James Philbin
3DH
334
13,123
0
12 Mar 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
  ImageNet Classification
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
VLM
280
18,587
0
06 Feb 2015
Do Deep Nets Really Need to be Deep?
Do Deep Nets Really Need to be Deep?
Lei Jimmy Ba
R. Caruana
160
2,117
0
21 Dec 2013
1