1 citations · 4 across the 9 of their papers we have counts for
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Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training
Saehyung Lee, Hyungyu Lee
Several recent studies have shown that the use of extra in-distribution data can lead to a high level of adversarial robustness. However, there is no guarantee that it will always…
Unconditional Image-Text Pair Generation with Multimodal Cross Quantizer
Hyungyung Lee, Sungjin Park, Joonseok Lee +1
Although deep generative models have gained a lot of attention, most of the existing works are designed for unimodal generation. In this paper, we explore a new method for uncondit…
Unidirectional Thin Adapter for Efficient Adaptation of Deep Neural Networks
Han Gyel Sun, Hyunjae Ahn, HyunGyu Lee +1
In this paper, we propose a new adapter network for adapting a pre-trained deep neural network to a target domain with minimal computation. The proposed model, unidirectional thin…
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization
Saehyung Lee, Hyungyu Lee, Sungroh Yoon
Adversarial examples cause neural networks to produce incorrect outputs with high confidence. Although adversarial training is one of the most effective forms of defense against ad…