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Found 11 papers, 6 papers with code
Universal Novelty Detection Through Adaptive Contrastive Learning
More precisely, for novelty detection, distribution shifts may occur in the training set or the test set.
Mitigating Overconfidence in Out-of-Distribution Detection by Capturing Extreme Activations
Detecting out-of-distribution (OOD) instances is crucial for the reliable deployment of machine learning models in real-world scenarios.
Spuriosity Rankings for Free: A Simple Framework for Last Layer Retraining Based on Object Detection
A promising solution to this problem is last-layer retraining, which involves retraining the linear classifier head on a small subset of data without spurious cues.
Blacksmith: Fast Adversarial Training of Vision Transformers via a Mixture of Single-step and Multi-step Methods
Despite the remarkable success achieved by deep learning algorithms in various domains, such as computer vision, they remain vulnerable to adversarial perturbations.
Seeking Next Layer Neurons' Attention for Error-Backpropagation-Like Training in a Multi-Agent Network Framework
Despite considerable theoretical progress in the training of neural networks viewed as a multi-agent system of neurons, particularly concerning biological plausibility and decentralized training, their applicability to real-world problems remains limited due to scalability issues.
Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Data
Despite their success, Machine Learning (ML) models do not generalize effectively to data not originating from the training distribution.
A Data-Centric Approach for Improving Adversarial Training Through the Lens of Out-of-Distribution Detection
For this purpose, we first demonstrate that the existing model-based methods can be equivalent to applying smaller perturbation or optimization weights to the hard training examples.
Your Out-of-Distribution Detection Method is Not Robust!
Therefore, unlike OOD detection in the standard setting, access to OOD, as well as in-distribution, samples sounds necessary in the adversarial training setup.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
PIAT: Physics Informed Adversarial Training for Solving Partial Differential Equations
In this paper, we propose the physics informed adversarial training (PIAT) of neural networks for solving nonlinear differential equations (NDE).
OOD Augmentation May Be at Odds with Open-Set Recognition
Despite advances in image classification methods, detecting the samples not belonging to the training classes is still a challenging problem.
Lagrangian Objective Function Leads to Improved Unforeseen Attack Generalization in Adversarial Training
However, it usually fails against other attacks, i. e. the model overfits to the training attack scheme.
