Yinpeng Dong

Improving Interpretability of Deep Neural Networks with Semantic Information

Interpretability of deep neural networks (DNNs) is essential since it enables users to understand the overall strengths and weaknesses of the models, conveys an understanding of how the models will behave in the future, and how to diagnose and correct potential problems. However, it is challenging to reason about what a DNN actually does due to its opaque or black-box nature. To address this issue, we propose a novel technique to improve the interpretability of DNNs by leveraging the rich semantic information embedded in human descriptions. By concentrating on the video captioning task, we first extract a set of semantically meaningful topics from the human descriptions that cover a wide range of visual concepts, and integrate them into the model with an interpretive loss. We then propose a prediction difference maximization algorithm to interpret the learned features of each neuron. Experimental results demonstrate its effectiveness in video captioning using the interpretable features, which can also be transferred to video action recognition. By clearly understanding the learned features, users can easily revise false predictions via a human-in-the-loop procedure.

Adversarial Attacks and Defences Competition

To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the structure and organization of the competition and the solutions developed by several of the top-placing teams.

Efficient and robust semi-supervised learning over a sparse-regularized graph

Graph-based Semi-Supervised Learning (GSSL) has limitations in widespread applicability due to its computationally prohibitive large-scale inference, sensitivity to data incompleteness, and incapability on handling time-evolving characteristics in an open set. To address these issues, we propose a novel GSSL based on a batch of informative beacons with sparsity appropriately harnessed, rather than constructing the pairwise affinity graph between the entire original samples. Specifically, (1) beacons are placed automatically by unifying the consistence of both data features and labels, which subsequentially act as indicators during the inference; (2) leveraging the information carried by beacons, the sample labels are interpreted as the weighted combination of a subset of characteristics-specified beacons; (3) if unfamiliar samples are encountered in an open set, we seek to expand the beacon set incrementally and update their parameters by incorporating additional human interventions if necessary. Experimental results on real datasets validate that our algorithm is effective and efficient to implement scalable inference, robust to sample corruptions, and capable to boost the performance incrementally in an open set by updating the beacon-related parameters.

Forecast the Plausible Paths in Crowd Scenes

Forecasting the future plausible paths of pedestrians in crowd scenes is of wide applications, but it still remains as a challenging task due to the complexities and uncertainties of crowd motions. To address these issues, we propose to explore the inherent crowd dynamics via a social-aware recurrent Gaussian process model, which facilitates the path prediction by taking advantages of the interplay between the rich prior knowledge and motion uncertainties. Specifically, we derive a social-aware LSTM to explore the crowd dynamic, resulting in a hidden feature embedding the rich prior in massive data. Afterwards, we integrate the descriptor into deep Gaussian processes with motion uncertainties appropriately harnessed. Crowd motion forecasting is implemented by regressing relative motion against the current positions, yielding the predicted paths based on a functional object associated with a distribution. Extensive experiments on public datasets demonstrate that our method obtains the state-of-the-art performance in both structured and unstructured scenes by exploring the complex and uncertain motion patterns, even if the occlusion is serious or the observed trajectories are noisy.