Convolutional neural networks towards less supervision for visual recognition
This thesis investigates convolutional neural networks for visual recognition. Recent convolutional neural networks have demonstrated excellent performance for a variety of recognition tasks but typically require large amounts of manually annotated training data to perform well. This data is often costly to annotate and may introduce unwanted biases. In this thesis we investigate different ways how to reduce the amount and complexity of required training supervision. In our first contribution, we propose a transfer learning approach with a convolutional neural network for object classification. We first learn mid level features on the large ImageNet dataset during a pre training phase, then we use the parameters to initialize another network designed for a smaller scale task, where less training data is available. We show, first, that the image representations can be efficiently transferred to other visual recognition tasks, and second, that these representations lead to higher performance when more data is used for pre training. We demonstratethat the proposed approach outperforms state of the art on the Pascal VOC image classification task. In our second contribution, we investigate weakly supervised learning for object recognition. We use the fact that for classification, convolutional neural networks tend to take decisions based on the most distinctive parts of objects. This allows us to build a network that can predict the location of objects, based on a weakly annotated dataset indicating only the presence or absence of objects but not their location in images. We demonstrate that our approach improves the state of theart on the Pascal VOC image classification task, performing on par with methods requiring full object level supervision. In our third contribution, we look at possible paths for progress in unsupervised learning with neural networks. We study the recent Generative Adversarial Networks; these architectures learn distributions of images and generate new samples, but the evaluation which learned model is better than others is difficult. We propose a twosample test method for this evaluation problem, allowing us to perform a first level of model selection. We investigate possible links between Generative Adversarial Networks and concepts related to causality, and propose a two sample test method for the task of causal discovery, outperforming the state of the art. Finally, building on a recent connection with optimal transport, we investigate what these generative algorithms are learning from unlabeled data.