论文标题
困难的模拟器用于开放式识别
Difficulty-Aware Simulator for Open Set Recognition
论文作者
论文摘要
开放式识别（OSR）假设未知实例在推理时间出现在蓝色中。 OSR的主要挑战是模型对未知数的响应是完全无法预测的。此外，由于实例的难度级别不同，因此开放式设置的多样性变得更加困难。因此，我们提出了一个新颖的框架，难以感知的模拟器（DIAS），该框架产生了具有不同难度水平的假货来模拟现实世界。我们首先在分类器的角度研究了生成对抗网络（GAN）的假货，并观察到这些伪造并不具有挑战性。这使我们通过对具有中等难题的甘恩产生的样品来定义难度的标准。为了产生难题的示例，我们介绍模仿者，模仿分类器的行为。此外，我们的修改后的gan和模仿者也分别产生了中等和易于缺陷的样本。结果，DIAS的表现优于AUROC和F分数指标的最先进方法。我们的代码可在https://github.com/wjun0830/difficulty-aware-simulator上找到。
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.
