The remarkable success of the Transformer model in Natural Language Processing (NLP) is increasingly capturing the attention of vision researchers in contemporary times. The Vision Transformer (ViT) model effectively models long-range dependencies while utilizing a self-attention mechanism by converting image information into meaningful representations. Moreover, the parallelism property of ViT ensures better scalability and model generalization compared to Recurrent Neural Networks (RNN). However, developing robust ViT models for high-risk vision applications, such as self-driving cars, is critical. Deterministic ViT models are susceptible to noise and adversarial attacks and incapable of yielding a level of confidence in output predictions. Quantifying the confidence (or uncertainty) level in the decision is highly important in such real-world applications. In this work, we introduce a probabilistic framework for ViT to quantify the level of uncertainty in the model's decision. We approximate the posterior distribution of network parameters using variational inference. While progressing through non-linear layers, the first-order Taylor approximation was deployed. The developed framework propagates the mean and covariance of the posterior distribution through layers of the probabilistic ViT model and quantifies uncertainty at the output predictions. Quantifying uncertainty aids in providing warning signals to real-world applications in case of noisy situations. Experimental results from extensive simulation conducted on numerous benchmark datasets (e.g., MNIST and Fashion-MNIST) for image classification tasks exhibit 1) higher accuracy of proposed probabilistic ViT under noise or adversarial attacks compared to the deterministic ViT. 2) Self-evaluation through uncertainty becomes notably pronounced as noise levels escalate. Simulations were conducted at the Texas Advanced Computing Center (TACC) on the Lonestar6 supercomputer node. With the help of this vital resource, we completed all the experiments within a reasonable period.
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Robustness Analysis for Convolutional Neural Networks with Uncertainty Quantification
This paper presents a novel framework for training convolutional neural networks (CNNs) to quantify the impact of gradual and abrupt uncertainties in the form of adversarial attacks. Uncertainty quantification is achieved by combining the CNN with a Gaussian process (GP) classifier algorithm. The variance of the GP quantifies the impact on the uncertainties and especially their effect on the object classification tasks. Learning from uncertainty provides the proposed CNN-GP framework with flexibility, reliability and robustness to adversarial attacks. The proposed approach includes training the network under noisy conditions. This is accomplished by comparing predictions with classification labels via the Kullback-Leibler divergence, Wasserstein distance and maximum correntropy. The network performance is tested on the classical MNIST, Fashion-MNIST, CIFAR10 and CIFAR 100 datasets. Further tests on robustness to both black-box and white-box attacks are also carried out for MNIST. The results show that the testing accuracy improves for networks that backpropogate uncertainty as compared to methods that do not quantify the impact of uncertainties. A comparison with a state-of-art Monte Carlo dropout method is also presented and the outperformance of the CNN-GP framework with respect to reliability and computational efficiency is demonstrated.
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- Award ID(s):
- 1903466
- NSF-PAR ID:
- 10259931
- Date Published:
- Journal Name:
- Proc. of the International Forum on Signal Processing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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