SHIELD: Defending Deep Neural Networks from Adversarial Attacks

by Nilaksh Das, PhD student at Georgia Institute of Technology in the School of Computational Science and Engineering. Das is advised by Polo Chau. “SHIELD is a fast and practical approach to defend deep neural networks from adversarial attacks. This work proposes a multifaceted framework which incorporates compression, randomization, model-retraining, and ensembling to make computer vision models robust to adversarial... Continue Reading →

Convergence of Value Aggregation for Imitation Learning

The following describes work by Ching-An Cheng and Byron Boots, which was awarded Best Paper at the Further details and proofs are available at The 21st International Conference on Artificial Intelligence and Statistics (AISTATS). The paper can be found here: https://arxiv.org/abs/1801.07292. Learning to make sequential decisions is a fundamental topic in designing automatic agents with artificial intelligence.... Continue Reading →

From Object Interactions to Fine-grained Video Understanding

Video understanding tasks such as action recognition and caption generation are crucial for various real-world applications in surveillance, video retrieval, human behavior understanding, etc. In this work, we present a generic recurrent module to detect relationships and interactions between arbitrary object groups for fine-grained video understanding. Our work is applicable to various open domain video... Continue Reading →

Visualizing Deep Learning Models at Facebook

This post summarizes the latest joint research between researchers at Georgia Tech and  Facebook on using visualization to make sense of deep learning models, published at IEEE VIS’17, a top visualization conference. While powerful deep learning models have significantly improved prediction accuracy, understanding these models remains a big challenge. Deep learning models are more difficult... Continue Reading →

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