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Raising the Bar on the Evaluation of Out-of-Distribution Detection
Jishnu Mukhoti, Tsung-Yu Lin, Bor-Chun Chen +4
In image classification, a lot of development has happened in detecting out-of-distribution (OoD) data. However, most OoD detection methods are evaluated on a standard set of datas…
Query-based Hard-Image Retrieval for Object Detection at Test Time
Edward Ayers, Jonathan Sadeghi, John Redford +2
There is a longstanding interest in capturing the error behaviour of object detectors by finding images where their performance is likely to be unsatisfactory. In real-world applic…
An Impartial Take to the CNN vs Transformer Robustness Contest
Francesco Pinto, Philip H. S. Torr, Puneet K. Dokania
Following the surge of popularity of Transformers in Computer Vision, several studies have attempted to determine whether they could be more robust to distribution shifts and provi…
Sample-dependent Adaptive Temperature Scaling for Improved Calibration
Tom Joy, Francesco Pinto, Ser-Nam Lim +2
It is now well known that neural networks can be wrong with high confidence in their predictions, leading to poor calibration. The most common post-hoc approach to compensate for t…
RegMixup: Mixup as a Regularizer Can Surprisingly Improve Accuracy and Out Distribution Robustness
Francesco Pinto, Harry Yang, Ser-Nam Lim +2
We show that the effectiveness of the well celebrated Mixup [Zhang et al., 2018] can be further improved if instead of using it as the sole learning objective, it is utilized as an…
Catastrophic overfitting can be induced with discriminative non-robust features
Guillermo Ortiz-Jiménez, Pau de Jorge, Amartya Sanyal +5
Adversarial training (AT) is the de facto method for building robust neural networks, but it can be computationally expensive. To mitigate this, fast single-step attacks can be use…
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
Pau de Jorge, Adel Bibi, Riccardo Volpi +4
Recently, Wong et al. showed that adversarial training with single-step FGSM leads to a characteristic failure mode named Catastrophic Overfitting (CO), in which a model becomes su…