26 citations · 54 across the 6 of their papers we have counts for
6 papers
Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversation
Beomsu Kim, Seokjun Seo, Seungju Han +2
Despite the remarkable performance of large-scale generative models in open-domain conversation, they are known to be less practical for building real-time conversation systems due…
Filter Style Transfer between Photos
Jonghwa Yim, Jisung Yoo, Won-joon Do +2
Over the past few years, image-to-image style transfer has risen to the frontiers of neural image processing. While conventional methods were successful in various tasks such as co…
Revisiting Classical Bagging with Modern Transfer Learning for On-the-fly Disaster Damage Detector
Junghoon Seo, Seungwon Lee, Beomsu Kim +1
Automatic post-disaster damage detection using aerial imagery is crucial for quick assessment of damage caused by disaster and development of a recovery plan. The main problem prev…
Bridging Adversarial Robustness and Gradient Interpretability
Beomsu Kim, Junghoon Seo, Taegyun Jeon
Adversarial training is a training scheme designed to counter adversarial attacks by augmenting the training dataset with adversarial examples. Surprisingly, several studies have o…
Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps
Beomsu Kim, Junghoon Seo, SeungHyun Jeon +3
Saliency Map, the gradient of the score function with respect to the input, is the most basic technique for interpreting deep neural network decisions. However, saliency maps are o…
Noise-adding Methods of Saliency Map as Series of Higher Order Partial Derivative
Junghoon Seo, Jeongyeol Choe, Jamyoung Koo +3
SmoothGrad and VarGrad are techniques that enhance the empirical quality of standard saliency maps by adding noise to input. However, there were few works that provide a rigorous t…