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cs.CV2025
Logit Mixture Outlier Exposure for Fine-grained Out-of-Distribution Detection
Akito Shinohara, Kohei Fukuda, Hiroaki Aizawa
The ability to detect out-of-distribution data is essential not only for ensuring robustness against unknown or unexpected input data but also for improving the generalization perf…
cs.CV2025
Class-wise Flooding Regularization for Imbalanced Image Classification
Hiroaki Aizawa, Yuta Naito, Kohei Fukuda
The purpose of training neural networks is to achieve high generalization performance on unseen inputs. However, when trained on imbalanced datasets, a model's prediction tends to…