Contrasting with adversarial examples improves self-supervised representation learning

Yuan, Meilu

Permalink

https://hdl.handle.net/2142/115961 Copy

Description

Title

Contrasting with adversarial examples improves self-supervised representation learning

Author(s)

Yuan, Meilu

Issue Date

2022-07-21

Director of Research (if dissertation) or Advisor (if thesis)

Koyejo, Oluwasanmi

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Self-Supervised Learning
• Representation Learning

Abstract

Lately, self-supervised contrastive learning has enjoyed enormous attention because of its good performance. This set of methods target at learning a good representation for downstream tasks by contrasting objects in different views. Such that, the distance between similar data’s representations can be minimized while representations of dissimilar data will become apart. Existing works suggest combining different meaningful transformations randomly to form positive examples, like, cropping, rotation, color distortion, blurring etc. As a special view of the data, specifically designed adversarial examples can maximize the loss values. Thus, they misguide the final prediction towards the wrongest direction. In this work, we explore the impacts of combining adversarially distorted examples as positive examples in self-supervised representation learning. By having more challenging positive examples in the contrasting stage, in the forward pass, we generate adversarially distorted images, extract representations of each anchor-adversarial data pair and calculate contrastive loss in the latent space; in the backward pass, we force the representation vectors of these two positive examples fully correlated. As adversarial attacks improve the feature invariance of learned representation, experimental results show adversarial attacks can further improve downstream task performance, robustness as well as the generalization ability of the trained encoder.

Graduation Semester

2022-08

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Meilu Yuan

Owning Collections