1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Multiplicative Logit Adjustment Approximates Neural-Collapse-Aware Decision Boundary Adjustment
Naoya Hasegawa, Issei Sato
Real-world data distributions are often highly skewed. This has spurred a growing body of research on long-tailed recognition, aimed at addressing the imbalance in training classif…
cs.CV2024★ 1 cited
Test-time Adaptation Meets Image Enhancement: Improving Accuracy via Uncertainty-aware Logit Switching
Shohei Enomoto, Naoya Hasegawa, Kazuki Adachi +4
Deep neural networks have achieved remarkable success in a variety of computer vision applications. However, there is a problem of degrading accuracy when the data distribution shi…
cs.LG2023★ 1 cited
Exploring Weight Balancing on Long-Tailed Recognition Problem
Naoya Hasegawa, Issei Sato
Recognition problems in long-tailed data, in which the sample size per class is heavily skewed, have gained importance because the distribution of the sample size per class in a da…