Learning to Select Pre-Trained Deep Representations with Bayesian Evidence Framework

We propose a Bayesian evidence framework to facilitate transfer learning from pre-trained deep convolutional neural networks (CNNs). Our framework is formulated on top of a least squares SVM (LS-SVM) classifier, which is simple and fast in both training and testing, and achieves competitive performance in practice. The regularization parameters in LS-SVM is estimated automatically without grid search and cross-validation by maximizing evidence, which is a useful measure to select the best performing CNN out of multiple candidates for transfer learning; the evidence is optimized efficiently by employing Aitken's delta-squared process, which accelerates convergence of fixed point update. The proposed Bayesian evidence framework also provides a good solution to identify the best ensemble of heterogeneous CNNs through a greedy algorithm. Our Bayesian evidence framework for transfer learning is tested on 12 visual recognition datasets and illustrates the state-of-the-art performance consistently in terms of prediction accuracy and modeling efficiency.Comment: Appearing in CVPR-2016 (oral presentation

Deep Collaborative Learning for Randomly Wired Neural Networks

A deep collaborative learning approach is introduced in which a chain of randomly wired neural networks is trained simultaneously to improve the overall generalization and form a strong ensemble model. The proposed method takes advantage of functional-preserving transfer learning and knowledge distillation to produce an ensemble model. Knowledge distillation is an effective learning scheme for improving the performance of small neural networks by using the knowledge learned by teacher networks. Most of the previous methods learn from one or more teachers but not in a collaborative way. In this paper, we created a chain of randomly wired neural networks based on a random graph algorithm and collaboratively trained the models using functional-preserving transfer learning, so that the small network in the chain could learn from the largest one simultaneously. The training method applies knowledge distillation between randomly wired models, where each model is considered as a teacher to the next model in the chain. The decision of multiple chains of models can be combined to produce a robust ensemble model. The proposed method is evaluated on CIFAR-10, CIFAR-100, and TinyImageNet. The experimental results show that the collaborative training significantly improved the generalization of each model, which allowed for obtaining a small model that can mimic the performance of a large model and produce a more robust ensemble approach