Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning

01/28/2022
by   Jie Zhang, et al.
0

Adversarial examples are inputs for machine learning models that have been designed by attackers to cause the model to make mistakes. In this paper, we demonstrate that adversarial examples can also be utilized for good to improve the performance of imbalanced learning. We provide a new perspective on how to deal with imbalanced data: adjust the biased decision boundary by training with Guiding Adversarial Examples (GAEs). Our method can effectively increase the accuracy of minority classes while sacrificing little accuracy on majority classes. We empirically show, on several benchmark datasets, our proposed method is comparable to the state-of-the-art method. To our best knowledge, we are the first to deal with imbalanced learning with adversarial examples.

READ FULL TEXT
research
10/02/2018

Adversarial Examples - A Complete Characterisation of the Phenomenon

We provide a complete characterisation of the phenomenon of adversarial ...
research
11/19/2015

A Unified Gradient Regularization Family for Adversarial Examples

Adversarial examples are augmented data points generated by imperceptibl...
research
08/26/2018

Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge

Adversarial examples are inputs to machine learning models designed to c...
research
12/19/2017

HotFlip: White-Box Adversarial Examples for NLP

Adversarial examples expose vulnerabilities of machine learning models. ...
research
04/13/2023

Adversarial Examples from Dimensional Invariance

Adversarial examples have been found for various deep as well as shallow...
research
05/28/2019

Brain-inspired reverse adversarial examples

A human does not have to see all elephants to recognize an animal as an ...
research
06/22/2023

Adversarial Resilience in Sequential Prediction via Abstention

We study the problem of sequential prediction in the stochastic setting ...

Please sign up or login with your details

Forgot password? Click here to reset