Out-of-distribution (OOD) detection methods have been developed to identify objects that a model has not seen during training. The Outlier Exposure (OE) methods use auxiliary datasets to train OOD detectors directly. However, the collection and learning of representative OOD samples may pose challenges. To tackle these issues, we propose the Outlier Aware Metric Learning (OAML) framework. The main idea of our method is to use the k-NN algorithm and Stable Diffusion model to generate outliers for training at the feature level without making any distributional assumptions. To increase feature discrepancies in the semantic space, we develop a mutual information-based contrastive learning approach for learning from OOD data effectively. Both theoretical and empirical results confirm the effectiveness of this contrastive learning technique. Furthermore, we incorporate knowledge distillation into our learning framework to prevent degradation of in-distribution classification accuracy. The combination of contrastive learning and knowledge distillation algorithms significantly enhances the performance of OOD detection. Experimental results across various datasets show that our method significantly outperforms previous OE methods.

Automated Machine Learning (AutoML) has been used successfully in settings where the learning task is assumed to be static. In many real-world scenarios, however, the data distribution will evolve over time, and it is yet to be shown whether AutoML techniques can effectively design online pipelines in dynamic environments. This study aims to automate pipeline design for online learning while continuously adapting to data drift. For this purpose, we design an adaptive Online Automated Machine Learning (OAML) system, searching the complete pipeline configuration space of online learners, including preprocessing algorithms and ensembling techniques. This system combines the inherent adaptation capabilities of online learners with the fast automated pipeline (re)optimization capabilities of AutoML. Focusing on optimization techniques that can adapt to evolving objectives, we evaluate asynchronous genetic programming and asynchronous successive halving to optimize these pipelines continually. We experiment on real and artificial data streams with varying types of concept drift to test the performance and adaptation capabilities of the proposed system. The results confirm the utility of OAML over popular online learning algorithms and underscore the benefits of continuous pipeline redesign in the presence of data drift.