A Caltech Library Service

When Does Contrastive Visual Representation Learning Work?

Cole, Elijah and Yang, Xuan and Wilber, Kimberly and Mac Aodha, Oisin and Belongie, Serge (2022) When Does Contrastive Visual Representation Learning Work? . (Unpublished)

This is the latest version of this item.

[img] PDF - Accepted Version
See Usage Policy.


Use this Persistent URL to link to this item:


Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised learning on ImageNet classification. While the particulars of pretraining on ImageNet are now relatively well understood, the field still lacks widely accepted best practices for replicating this success on other datasets. As a first step in this direction, we study contrastive self-supervised learning on four diverse large-scale datasets. By looking through the lenses of data quantity, data domain, data quality, and task granularity, we provide new insights into the necessary conditions for successful self-supervised learning. Our key findings include observations such as: (i) the benefit of additional pretraining data beyond 500k images is modest, (ii) adding pretraining images from another domain does not lead to more general representations, (iii) corrupted pretraining images have a disparate impact on supervised and self-supervised pretraining, and (iv) contrastive learning lags far behind supervised learning on fine-grained visual classification tasks.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Cole, Elijah0000-0001-6623-0966
Wilber, Kimberly0000-0001-7040-0251
Mac Aodha, Oisin0000-0002-5787-5073
Belongie, Serge0000-0002-0388-5217
Contact Email
Additional Information:We thank Mason McGill for detailed feedback, and Grant Van Horn, Christine Kaeser-Chen, Yin Cui, Sergey Ioffe, Pietro Perona, and the rest of the Perona Lab for insightful discussions. This work was supported by the Caltech Resnick Sustainability Institute, an NSF Graduate Research Fellowship (grant number DGE1745301), and the Pioneer Centre for AI (DNRF grant number P1).
Group:Resnick Sustainability Institute
Funding AgencyGrant Number
Resnick Sustainability InstituteUNSPECIFIED
NSF Graduate Research FellowshipDGE-1745301
Danish National Research FoundationDNRF-P1
Record Number:CaltechAUTHORS:20220406-160758984
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:114160
Deposited By: Elijah Cole
Deposited On:06 Apr 2022 17:28
Last Modified:06 Apr 2022 17:28

Available Versions of this Item

Repository Staff Only: item control page