16 citations · 24 across the 5 of their papers we have counts for
5 papers
GTA: Guided Transfer of Spatial Attention from Object-Centric Representations
SeokHyun Seo, Jinwoo Hong, JungWoo Chae +2
Utilizing well-trained representations in transfer learning often results in superior performance and faster convergence compared to training from scratch. However, even if such go…
Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning
Jae-Hun Lee, Doyoung Yoon, ByeongMoon Ji +2
Linear probing (LP) (and -NN) on the upstream dataset with labels (e.g., ImageNet) and transfer learning (TL) to various downstream datasets are commonly employed to evaluate th…
Self-Knowledge Distillation with Progressive Refinement of Targets
Kyungyul Kim, ByeongMoon Ji, Doyoung Yoon +1
The generalization capability of deep neural networks has been substantially improved by applying a wide spectrum of regularization methods, e.g., restricting function space, injec…
Propagated Perturbation of Adversarial Attack for well-known CNNs: Empirical Study and its Explanation
Jihyeun Yoon, Kyungyul Kim, Jongseong Jang
Deep Neural Network based classifiers are known to be vulnerable to perturbations of inputs constructed by an adversarial attack to force misclassification. Most studies have focus…
Bin-wise Temperature Scaling (BTS): Improvement in Confidence Calibration Performance through Simple Scaling Techniques
Byeongmoon Ji, Hyemin Jung, Jihyeun Yoon +2
The prediction reliability of neural networks is important in many applications. Specifically, in safety-critical domains, such as cancer prediction or autonomous driving, a reliab…