391 citations · 1.4k across the 26 of their papers we have counts for
5 papers · 1 filter
Exploring the Limits of Out-of-Distribution Detection
Stanislav Fort, Jie Ren, Balaji Lakshminarayanan
Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We demonstrate that large-scale pre-trained transformers can significantly improve the state…
How many degrees of freedom do we need to train deep networks: a loss landscape perspective
Brett W. Larsen, Stanislav Fort, Nic Becker +1
A variety of recent works, spanning pruning, lottery tickets, and training within random subspaces, have shown that deep neural networks can be trained using far fewer degrees of f…
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
Jie Ren, Stanislav Fort, Jeremiah Liu +3
Mahalanobis distance (MD) is a simple and popular post-processing method for detecting out-of-distribution (OOD) inputs in neural networks. We analyze its failure modes for near-OO…
Drawing Multiple Augmentation Samples Per Image During Training Efficiently Decreases Test Error
Stanislav Fort, Andrew Brock, Razvan Pascanu +2
In computer vision, it is standard practice to draw a single sample from the data augmentation procedure for each unique image in the mini-batch. However recent work has suggested…
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
James Lucas, Juhan Bae, Michael R. Zhang +3
Linear interpolation between initial neural network parameters and converged parameters after training with stochastic gradient descent (SGD) typically leads to a monotonic decreas…