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14 papers · 2 filters
A Method for Computing Class-wise Universal Adversarial Perturbations
Tejus Gupta, Abhishek Sinha, Nupur Kumari +2
We present an algorithm for computing class-specific universal adversarial perturbations for deep neural networks. Such perturbations can induce misclassification in a large fracti…
A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddings
Qi Li, Long Mai, Michael A. Alcorn +1
Large, pre-trained generative models have been increasingly popular and useful to both the research and wider communities. Specifically, BigGANs a class-conditional Generative Adve…
REMIND Your Neural Network to Prevent Catastrophic Forgetting
Tyler L. Hayes, Kushal Kafle, Robik Shrestha +2
People learn throughout life. However, incrementally updating conventional neural networks leads to catastrophic forgetting. A common remedy is replay, which is inspired by how the…
DiffTaichi: Differentiable Programming for Physical Simulation
Yuanming Hu, Luke Anderson, Tzu-Mao Li +4
We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators. Based on an imperative programming langu…
Learning Robust Data Representation: A Knowledge Flow Perspective
Zhengming Ding, Ming Shao, Handong Zhao +1
It is always demanding to learn robust visual representation for various learning problems; however, this learning and maintenance process usually suffers from noise, incompletenes…
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
Nir Levine, Yinlam Chow, Rui Shu +3
Many real-world sequential decision-making problems can be formulated as optimal control with high-dimensional observations and unknown dynamics. A promising approach is to embed t…