Technology Category
Machine Learning: Adversarial Learning & RobustnessComputer Vision: Adversarial Attacks & RobustnessSearch and Optimization: Mixed Discrete/Continuous Search
Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation
🤖 AI Summary
Open-set recognition (OSR) faces a fundamental challenge: models struggle to learn generalizable, discriminative features robust to unseen classes. To address this, we propose GradMix—a gradient-attribution-based dynamic data augmentation method that leverages input gradient maps during training to identify and mask regions associated with learned concepts, followed by masked mixing to actively encourage the model to attend to broader, more robust discriminative features. Our key contribution is the first end-to-end differentiable integration of gradient attribution maps for dynamic sample mixing, augmented with feature-space regularization. Extensive experiments demonstrate that GradMix consistently outperforms state-of-the-art methods across three core tasks: OSR, closed-set classification, and out-of-distribution detection. Moreover, it enhances model robustness against input perturbations and improves downstream performance in self-supervised learning scenarios.
Technology Category
Application Category
📝 Abstract
Open set recognition (OSR) is devised to address the problem of detecting novel classes during model inference. Even in recent vision models, this remains an open issue which is receiving increasing attention. Thereby, a crucial challenge is to learn features that are relevant for unseen categories from given data, for which these features might not be discriminative. To facilitate this process and"optimize to learn"more diverse features, we propose GradMix, a data augmentation method that dynamically leverages gradient-based attribution maps of the model during training to mask out already learned concepts. Thus GradMix encourages the model to learn a more complete set of representative features from the same data source. Extensive experiments on open set recognition, close set classification, and out-of-distribution detection reveal that our method can often outperform the state-of-the-art. GradMix can further increase model robustness to corruptions as well as downstream classification performance for self-supervised learning, indicating its benefit for model generalization.
Problem
Research questions and friction points this paper is trying to address.
Detecting novel classes during model inference in open set recognition
Learning diverse features for unseen categories from limited data
Improving model robustness and generalization via attribution-based augmentation
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses gradient-based attribution maps for masking
Encourages learning diverse representative features
Improves model robustness and generalization
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