Probing the State of the Art: A Critical Look at Visual Representation
Evaluation

v1v2 (latest)

Probing the State of the Art: A Critical Look at Visual Representation Evaluation

30 November 2019

Joan Bruna

ArXiv (abs)PDF HTML

Papers citing "Probing the State of the Art: A Critical Look at Visual Representation Evaluation"

11 / 11 papers shown

Title
Probing Graph Representations Mohammad Sadegh Akhondzadeh Vijay Lingam Aleksandar Bojchevski 95 10 0 07 Mar 2023
Evaluating Representations with Readout Model Switching Yazhe Li J. Bornschein Marcus Hutter 63 0 0 19 Feb 2023
Evaluating Self-Supervised Learning for Molecular Graph Embeddings Hanchen Wang Jean Kaddour Shengchao Liu Jian Tang Joan Lasenby Qi Liu 121 23 0 16 Jun 2022
On the Origins of the Block Structure Phenomenon in Neural Network Representations Thao Nguyen M. Raghu Simon Kornblith 88 13 0 15 Feb 2022
Socially Supervised Representation Learning: the Role of Subjectivity in Learning Efficient Representations Julius Taylor Eleni Nisioti Clément Moulin-Frier 46 0 0 20 Sep 2021
Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articles Jevgenij Gamper Nasir M. Rajpoot 69 65 0 08 Mar 2021
Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth Thao Nguyen M. Raghu Simon Kornblith OOD 96 283 0 29 Oct 2020
Evaluating representations by the complexity of learning low-loss predictors William F. Whitney M. Song David Brandfonbrener Jaan Altosaar Kyunghyun Cho 76 24 0 15 Sep 2020
Self-Supervised Learning for Large-Scale Unsupervised Image Clustering Evgenii Zheltonozhskii Chaim Baskin A. Bronstein A. Mendelson SSL 52 10 0 24 Aug 2020
Transfer Learning or Self-supervised Learning? A Tale of Two Pretraining Paradigms Xingyi Yang Xuehai He Yuxiao Liang Yue Yang Shanghang Zhang P. Xie SSL 69 43 0 19 Jun 2020
How Useful is Self-Supervised Pretraining for Visual Tasks? Alejandro Newell Jia Deng SSL 70 139 0 31 Mar 2020