ODIN (3) 썸네일형 리스트형 [논문리뷰] Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy 논문링크 : https://arxiv.org/abs/1908.04951 Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy Since deep learning models have been implemented in many commercial applications, it is important to detect out-of-distribution (OOD) inputs correctly to maintain the performance of the models, ensure the quality of the collected data, and prevent the appl arxiv.org 해당 논문은 ID데이터만을.. [논문리뷰] A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks 논문 링크 :https://arxiv.org/abs/1807.03888 A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks Detecting test samples drawn sufficiently far away from the training distribution statistically or adversarially is a fundamental requirement for deploying a good classifier in many real-world machine learning applications. However, deep neural networks wi arxiv.or.. [논문리뷰] Enhancing The Reliability of Out-Of-Distribution Image Detection In Neural Networks 논문 링크 : https://arxiv.org/abs/1706.02690 Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks We consider the problem of detecting out-of-distribution images in neural networks. We propose ODIN, a simple and effective method that does not require any change to a pre-trained neural network. Our method is based on the observation that using temperatu arxiv.org 해당 논문은.. 이전 1 다음
