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Simple and Effective Out-of-Distribution Detection via Cosine-based Softmax Loss
Deep learning models need to detect out-of-distribution (OOD) data in the inference stage because they are trained to estimate the train distribution and infer the data sampled from the distribution.
Propagation Regularizer for Semi-Supervised Learning With Extremely Scarce Labeled Samples
The proposed methods show 70. 9%, 30. 3%, and 78. 9% accuracy on CIFAR-10, CIFAR-100, SVHN dataset with just one labeled sample per class, which are improved by 8. 9% to 120. 2% compared to the existing approaches.
A Behavior Analysis-Based Game Bot Detection Approach Considering Various Play Styles
Then, it develops a local bot detection model for each player group.
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