Effects of Degradations on Deep Neural Network Architectures

By Prasun Roy, Subhankar Ghosh, Saumik Bhattacharya, Umapada Pal. 2018

167 citations recorded by the source corpus.

Recently, image classification methods based on capsules (groups of neurons) and a novel dynamic routing protocol are proposed. The methods show promising performances than the state-of-the-art CNN-based models in some of the existing datasets. However, the behavior of capsule-based models and CNN-based models are largely unknown in presence of noise. So it is important to study the performance of these models under various noises. In this paper, we demonstrate the effect of image degradations on deep neural network architectures for image classification task. We select six widely used CNN architectures to analyse their performances for image classification task on datasets of various distortions. Our work has three main contributions: 1) we observe the effects of degradations on different CNN models; 2) accordingly, we propose a network setup that can enhance the robustness of any CNN architecture for certain degradations, and 3) we propose a new capsule network that achieves high recognition accuracy. To the best of our knowledge, this is the first study on the performance of CapsuleNet (CapsNet) and other state-of-the-art CNN architectures under different types of image degradations. Also, our datasets and source code are available publicly to the researchers.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Safety, Robustness & Interpretability

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Citations in the corpus

On Robustness and Transferability of Convolutional Neural Networks (2020, background): “Subsequent studies have investigated brittleness to similar corruptions [53, 76], as well as to impulse noise [31], photometric perturbations [62], and small shifts and other transformations [2, 17, 74].”
Toward Causal Representation Learning (2021, background): “So far, there has been no deﬁnitive consensus on how to solve these problems, although progress has been made using data augmentation, pretraining, self-supervision, and architectures with suitable inductive biases with respect to a perturbation of interest [60], [64], [137], [170], [206], [233].”
Towards Causal Representation Learning (2021, background): “In computer vision [75, 228], changes in the test distribution may, for instance, come from aberrations like camera blur, noise or compression quality [106, 129, 170, 206], or from shifts, rotations, or viewpoints [7, 11, 63, 282].”
Online Continual Learning in Image Classification: An Empirical Survey (2021, positive): “Similar to [35, 11], we use the reduced ResNet18 [110] as the base model for all datasets and methods.”
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization (2021, background): “Finally, SYN-D (Roy et al., 2018) (SD) is a synthetic dataset, which is generated by combining noisy and complex backgrounds.”
Learn from Others and Be Yourself in Heterogeneous Federated Learning (2022, background): “Speciﬁ-cally, Digits task includes four domains ( i.e ., MNIST ( M ), USPS ( U ), SVHN ( SV ) and SYN ( SY )) with 10 categories.”