Quality Resilient Deep Neural Networks

Abstract

We study deep neural networks for classification of images with quality distortions. We first show that networks fine-tuned on distorted data greatly outperform the original networks when tested on distorted data. However, fine-tuned networks perform poorly on quality distortions that they have not been trained for. We propose a mixture of experts ensemble method that is robust to different types of distortions. The "experts" in our model are trained on a particular type of distortion. The output of the model is a weighted sum of the expert models, where the weights are determined by a separate gating network. The gating network is trained to predict optimal weights for a particular distortion type and level. During testing, the network is blind to the distortion level and type, yet can still assign appropriate weights to the expert models. We additionally investigate weight sharing methods for the mixture model and show that improved performance can be achieved with a large reduction in the number of unique network parameters.

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• Picking Up Quantization Steps for Compressed Image Classification
• Towards Corruption-Agnostic Robust Domain Adaptation
• One-Shot Item Search with Multimodal Data
• Towards Robust Neural Networks with Lipschitz Continuity
• Wiggling Weights to Improve the Robustness of Classifiers
• Trained Model Fusion for Object Detection using Gating Network