For models trained on-premise but deployed in a distributed fashion across multiple entities, we demonstrate that minimizing distance correlation between sensitive data such as faces and intermediary representations enables prediction while preventing reconstruction attacks. Leakage (measured using distance correlation between input and intermediate representations) is the risk associated with the reconstruction of raw face data from intermediary representations that are communicated in a distributed setting. We demonstrate on face datasets that our method is resilient to reconstruction attacks during distributed inference while maintaining information required to sustain good classification accuracy. We share modular code for performing NoPeek-Infer at http://tiny.cc/nopeek along with corresponding trained models for benchmarking attack techniques.

We present a new action recognition deep neural network which adaptively learns the best action velocities in addition to the classification. While deep neural networks have reached maturity for image understanding tasks, we are still exploring network topologies and features to handle the richer environment of video clips. Here, we tackle the problem of multiple velocities in action recognition, and provide state-of-the-art results for facial expression recognition, on known and new collected datasets. We further provide the training steps for our semi-supervised network, suited to learn from huge unlabeled datasets with only a fraction of labeled examples.

Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators to collect data. Utilizing this notion of small visual diversity, we revisit Maximum-Entropy learning in the context of fine-grained classification, and provide a training routine that maximizes the entropy of the output probability distribution for training convolutional neural networks on FGVC tasks. We provide a theoretical as well as empirical justification of our approach, and achieve state-of-the-art performance across a variety of classification tasks in FGVC, that can potentially be extended to any fine-tuning task. Our method is robust to different hyperparameter values, amount of training data and amount of training label noise and can hence be a valuable tool in many similar problems.