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Found 5 papers, 3 papers with code
PRIME: A few primitives can boost robustness to common corruptions
Despite their impressive performance on image classification tasks, deep networks have a hard time generalizing to unforeseen corruptions of their data.
Ranked #29 on
Domain Generalization
on ImageNet-C
Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off
While adversarial training has become the de facto approach for training robust classifiers, it leads to a drop in accuracy.
Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off
While adversarial training has become the de facto approach for training robust classifiers, it leads to a drop in accuracy.
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep Networks
Deep neural networks that yield human interpretable decisions by architectural design have lately become an increasingly popular alternative to post hoc interpretation of traditional black-box models.
Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models
For modelling we propose a novel semi-supervised algorithm called Fusion Hidden Markov Model (FHMM) which is more robust to noise, requires comparatively less training time, and utilizes the benefits of ensemble learning to better model temporal relationships in data.
