April 02, 2018
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 temperature scaling and adding small perturbations to the input can separate the softmax score distributions between in- and out-of-distribution images, allowing for more effective detection. We show in a series of experiments that ODIN is compatible with diverse network architectures and datasets. It consistently outperforms the baseline approach (Hendrycks & Gimpel, 2017) by a large margin, establishing a new state-of-the-art performance on this task. For example, ODIN reduces the false positive rate from the baseline 34.7% to 4.3% on the DenseNet (applied to CIFAR-10 and Tiny-ImageNet) when the true positive rate is 95%.
Written by
Yixuan Li
RAYADURGAM SRIKANT
Shiyu Liang
Publisher
ICLR
August 01, 2024
Ju-Chieh Chou, Wei-Ning Hsu, Karen Livescu, Arun Babu, Alexis Conneau, Alexei Baevski, Michael Auli
August 01, 2024
July 23, 2024
Llama team
July 23, 2024
June 25, 2024
Min-Jae Hwang, Ilia Kulikov, Benjamin Peloquin, Hongyu Gong, Peng-Jen Chen, Ann Lee
June 25, 2024
June 05, 2024
Robin San Romin, Pierre Fernandez, Hady Elsahar, Alexandre Deffosez, Teddy Furon, Tuan Tran
June 05, 2024
Foundational models
Latest news
Foundational models