Difficulty-Aware Simulator for Open Set Recognition
This work addresses the challenge of unpredictable model responses to unknown instances in open set recognition, which is crucial for real-world AI applications, though it is incremental in improving simulation methods.
The paper tackles the problem of open set recognition by proposing a difficulty-aware simulator (DIAS) that generates fake samples with varying difficulty levels to better simulate real-world unknowns, resulting in state-of-the-art performance with improved AUROC and F-score metrics.
Open set recognition (OSR) assumes unknown instances appear out of the blue at the inference time. The main challenge of OSR is that the response of models for unknowns is totally unpredictable. Furthermore, the diversity of open set makes it harder since instances have different difficulty levels. Therefore, we present a novel framework, DIfficulty-Aware Simulator (DIAS), that generates fakes with diverse difficulty levels to simulate the real world. We first investigate fakes from generative adversarial network (GAN) in the classifier's viewpoint and observe that these are not severely challenging. This leads us to define the criteria for difficulty by regarding samples generated with GANs having moderate-difficulty. To produce hard-difficulty examples, we introduce Copycat, imitating the behavior of the classifier. Furthermore, moderate- and easy-difficulty samples are also yielded by our modified GAN and Copycat, respectively. As a result, DIAS outperforms state-of-the-art methods with both metrics of AUROC and F-score. Our code is available at https://github.com/wjun0830/Difficulty-Aware-Simulator.