Leveraging Test-Time Consensus Prediction for Robustness against Unseen Noise

Sarkar, Anindya and Sarkar, Anirban and Balasubramanian, Vineeth N (2022) Leveraging Test-Time Consensus Prediction for Robustness against Unseen Noise. In: 22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022, 4 January 2022 through 8 January 2022, Waikoloa.

Text
Proceedings_2022_IEEE_CVF1.pdf - Published Version
Restricted to Registered users only
Download (2MB) | Request a copy

Abstract

We propose a method to improve DNN robustness against unseen noisy corruptions, such as Gaussian noise, Shot Noise, Impulse Noise, Speckle noise with different levels of severity by leveraging ensemble technique through a consensus based prediction method using self-supervised learning at inference time. We also propose to enhance the model training by considering other aspects of the issue i.e. noise in data and better representation learning which shows even better generalization performance with the consensus based prediction strategy. We report results of each noisy corruption on the standard CIFAR10-C and ImageNet-C benchmark which shows significant boost in performance over previous methods. We also introduce results for MNIST-C and TinyImagenet-C to show usefulness of our method across datasets of different complexities to provide robustness against unseen noise. We show results with different architectures to validate our method against other baseline methods, and also conduct experiments to show the usefulness of each part of our method. © 2022 IEEE.

[error in script]

IITH Creators: