no code implementations • 29 Sep 2021 • Hyungjun Joo, Seokhyeon Ha, Jae Myung Kim, Sungyeob Han, Jungwoo Lee
As deep learning has been successfully deployed in diverse applications, there is ever increasing need for explaining its decision.
no code implementations • 29 Sep 2021 • Tae Hyun Cho, Sungyeob Han, Heesoo Lee, Kyungjae Lee, Jungwoo Lee
Distributional reinforcement learning aims to learn distribution of return under stochastic environments.
no code implementations • 29 Sep 2021 • Sungyeob Han, Yeongmo Kim, Jungwoo Lee
The memory based continual learning stores a small subset of the data for previous tasks and applies various methods such as quadratic programming and sample selection.
no code implementations • 29 Sep 2021 • Jaehak Cho, Jae Myung Kim, Sungyeob Han, Jungwoo Lee
To address the issue, we propose a novel method that generates a union of disjoint PIs.
no code implementations • 1 Jan 2021 • Sungyeob Han, Yeongmo Kim, Jungwoo Lee
We also show that memory-based approaches have an inherent problem of overfitting to memory, which degrades the performance on previously learned tasks, namely catastrophic forgetting.
no code implementations • 25 Sep 2019 • Hyeungill Lee, Sungyeob Han, Jungwoo Lee
However, these adversarial attack methods used in these techniques are fixed, making the model stronger only to attacks used in training, which is widely known as an overfitting problem.
no code implementations • ICLR 2019 • Sungyeob Han, Daeyoung Kim, Jungwoo Lee
We propose a novel unsupervised classification method based on graph Laplacian.
no code implementations • 9 May 2017 • Hyeungill Lee, Sungyeob Han, Jungwoo Lee
The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image.