CVPR

Our paper Out-of-Distribution Detection with Adversarial Outlier Exposure has been accepted on the SAIAD Workshop at CVPR. Abstract § Machine learning models typically perform reliably only on inputs drawn from the distribution they were trained on, making Out-of-Distribution (OOD) detection essential for safety-critical applications. While exposing models to example outliers during training is one of the most effective ways to enhance OOD detection, recent studies suggest that synthetically …

Our paper, PyTorch-OOD: A library for Out-of-Distribution Detection based on PyTorch, has been presented at the CVPR 2022 Workshops. You can find the most recent version of the Python source code on GitHub. Abstract § Machine Learning models based on Deep Neural Networks behave unpredictably when presented with inputs that do not stem from the training distribution and sometimes make egregiously wrong predictions with high confidence. This property undermines the trustworthiness of systems …