Know Yourself Better: Diverse Object-Related Features Improve Open Set Recognition

Open-Set Domain Adaptation Under Background Distribution Shift: Challenges and A Provably Efficient Solution

arXiv:2512.01152v5 Announce Type: replace-cross Abstract: As we deploy machine learning systems in the real world, a core challenge is to maintain a model that is performant even as the data shifts....

Sparse Autoencoders for Interpretable Out-of-Distribution Detection

arXiv:2607. 12094v1 Announce Type: cross Abstract: Reliable detection of out-of-distribution (OOD) samples is crucial for the safe deployment of machine learning models.

Simple Domain Generalization Methods are Strong Baselines for Open Domain Generalization

arXiv:2303. 18031v2 Announce Type: replace-cross Abstract: In real-world applications, a machine learning model is required to handle an open-set recognition (OSR), where unknown classes appear during the inference, in addition to a domain shift, where the data distribution differs between the training and inference phases.

Open-World Semantic Segmentation with Sensitivity Modeling

arXiv:2608. 08308v1 Announce Type: cross Abstract: Modern vision systems must operate in "open-world" settings, where models must recognize known categories and detect unseen or anomalous content.

Efficient Neural Network Model Selection for Few-Class Application Datasets

arXiv:2606. 19712v1 Announce Type: new Abstract: While much effort has focused on developing and benchmarking high-performance neural networks, less attention has been given to how dataset properties, known to practitioners, can guide efficient model selection.

CloSeR: Unified Relational Distillation from Closed-Set Teachers for Category Discovery

Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention: given partially labelled data, the goal is to correctly recognize known classes while d...

huggingface_hub v1.0: Five Years of Building the Foundation of Open Machine Learning

Mitigating Simplicity Bias in OOD Detection through Object Co-occurrence Analysis

arXiv:2605. 07821v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is crucial for ensuring the reliability of deep learning models.

Vertical Fusion: Condensing Internal Representations for Robust ViT Classification

arXiv:2607. 10391v1 Announce Type: cross Abstract: Despite exposing rich intermediate representations, Vision Transformers (ViTs) are almost exclusively utilized as black-box feature extractors, where only the last layer is considered for downstream tasks.

Exploring the Rashomon Set for Concept-Based Models

arXiv:2511. 19636v2 Announce Type: replace-cross Abstract: In many machine learning problems, there may exist multiple models that achieve nearly identical predictive performance while relying on fundamentally different internal logic.

SmoothOperator: Enhancing Representations for Fine-grained Open-set Recognition via Modulated Label Smoothing

arXiv:2610.00851v1 Announce Type: new Abstract: Open Set Recognition (OSR) aims to enable models to accurately classify known classes while rejecting samples from unseen classes. A key challenge in O...

Towards Generalizable Deepfake Image Detection with Vision Transformers

arXiv:2604. 17376v2 Announce Type: replace-cross Abstract: In today's day and age, we face a challenge in detecting deepfake images because of the fast evolution of modern generative models and the poor generalization capability of existing methods.