Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· 0 citations· 35 references
TL;DR
This tutorial covers recent developments in OOD detection from both theoretical and practical perspectives, including four major categories: (1) post-hoc methods, (2) training-based methods with auxiliary outliers, (3) training methods without auxiliary outliers, and (4) foundation model-based approaches, along with recent advances in each area.
Abstract
Despite their remarkable success, Deep Neural Networks (DNNs) remain challenging to deploy in critical applications due to their inability to handle out-of-distribution (OOD) data. DNNs are trained assuming training and deployment data follow the same distribution, but this assumption is often violated in practice. When encountering unfamiliar inputs, models may produce highly confident yet incorrect predictions, and in high-stakes decision-making environments, such incorrect predictions can be costly. OOD detection has thus become fundamental across deep learning, affecting applications from computer vision and NLP to security, autonomous systems, and generative models. This tutorial covers recent developments in OOD detection from both theoretical and practical perspectives, including four major categories: (1) post-hoc methods, (2) training-based methods with auxiliary outliers, (3) training methods without auxiliary outliers, and (4) foundation model-based approaches, along with recent advances in each area.
A novel approach that leverages sparse autoencoders (SAEs) to learn interpretable features from these intermediate activations and proposes a new OOD score derived from the cosine similarity between the sparse feature activations of a test sample and the mean activations of ID classes.
Ayush Karmacharya, Luke Luschwitz, Lucia Romero et al.· 0 citations
This comprehensive survey formally defines the CTTA problem, analyzes the diverse continual domain shift patterns that characterize different evaluation protocols, and proposes a hierarchical taxonomy that categorizes existing methods into three families: optimization-based strategies (entropy minimization, pseudo-labeling, parameter restoration), parameter-efficient methods (normalization layer adaptation, adaptive parameter selection), and architecture-based approaches (teacher-student frameworks, adapters, visual prompting, masked modeling).
The findings demonstrate that introducing prior structure, through strategic sparse supervision, pre-trained multimodal knowledge, or transferable representations, consistently compensates for limited labeled data across diverse vision tasks.
In this paper, we propose MuST-VAD, a mutual structured learning framework for weakly supervised video anomaly detection (VAD) in which an anomaly detector and a large vision-language model (LVLM) exchange their acquired knowledge. Detectors in weakly supervised VAD learn anomaly scores from features extracted by a fixed, task-agnostic backbone. These fixed features bound the achievable detection accuracy. Recent methods therefore transfer LVLM semantics into the detector as richer features. However, this transfer is one-way: what the detector learns about the target videos never returns to the LVLM. MuST-VAD extends the one-way transfer into a bidirectional learning loop. In this loop, the latest detector predictions supervise the LVLM adaptation, and the adapted LVLM returns updated representations that retrain the detector; the two models alternate these updates over small video groups. Both models train on detector-selected key clips, while confidence weighting and annotation-anchored question answering keep the exchanged supervision reliable. On UCF-Crime, our mutual learning improves the one-pass transfer baseline from 88.15% to 88.63% AUROC and from 37.25% to 42.46% average precision (AP), outperforming the state-of-the-art method in AP by 4.13 points.
Satoshi Hashimoto, Hitoshi Nishimura, Mori Kurokawa· 0 citations
Overall, this dissertation provides a unified investigation into data imbalance, data quality, and data scarcity-three core bottlenecks of modern deep learning-and proposes principled solutions that improve robustness, interpretability, and efficiency across both CV and NLP domains.
A straightforward feature mixing approach for pre-trained Transformers is presented, which combines multi-layer representations via calculated importance weights, and identifies OOD samples using Mahalanobis distance in the blended feature space.
Tong Wei, Bolin Wang, Jiang-Xin Shi et al.· Neural Information Processin...· 8 citations· ⚡2