Generalized out-of-distribution detection: A survey
Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …
machine learning systems. For instance, in autonomous driving, we would like the driving …
Generalized out-of-distribution detection and beyond in vision language model era: A survey
Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine
learning systems and has shaped the field of OOD detection. Meanwhile, several other …
learning systems and has shaped the field of OOD detection. Meanwhile, several other …
Recent Advances in OOD Detection: Problems and Approaches
S Lu, Y Wang, L Sheng, A Zheng, L He… - arXiv preprint arXiv …, 2024 - arxiv.org
Out-of-distribution (OOD) detection aims to detect test samples outside the training category
space, which is an essential component in building reliable machine learning systems …
space, which is an essential component in building reliable machine learning systems …
Large language models for anomaly and out-of-distribution detection: A survey
R Xu, K Ding - arXiv preprint arXiv:2409.01980, 2024 - arxiv.org
Detecting anomalies or out-of-distribution (OOD) samples is critical for maintaining the
reliability and trustworthiness of machine learning systems. Recently, Large Language …
reliability and trustworthiness of machine learning systems. Recently, Large Language …
DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection
Out-of-distribution (OOD) detection is essential for ensuring the robustness of machine
learning models by identifying samples that deviate from the training distribution. While …
learning models by identifying samples that deviate from the training distribution. While …
Dota: Distributional test-time adaptation of vision-language models
Vision-language foundation models (eg, CLIP) have shown remarkable performance across
a wide range of tasks. However, deploying these models may be unreliable when significant …
a wide range of tasks. However, deploying these models may be unreliable when significant …
Out-Of-Distribution Detection with Diversification (Provably)
Out-of-distribution (OOD) detection is crucial for ensuring reliable deployment of machine
learning models. Recent advancements focus on utilizing easily accessible auxiliary outliers …
learning models. Recent advancements focus on utilizing easily accessible auxiliary outliers …
The Best of Both Worlds: On the Dilemma of Out-of-distribution Detection
Out-of-distribution (OOD) detection is essential for model trustworthiness which aims to
sensitively identify semantic OOD samples and robustly generalize for covariate-shifted …
sensitively identify semantic OOD samples and robustly generalize for covariate-shifted …
Self-Calibrated Tuning of Vision-Language Models for Out-of-Distribution Detection
Out-of-distribution (OOD) detection is crucial for deploying reliable machine learning models
in open-world applications. Recent advances in CLIP-based OOD detection have shown …
in open-world applications. Recent advances in CLIP-based OOD detection have shown …
Towards Few-Shot Learning in the Open World: A Review and Beyond
Human intelligence is characterized by our ability to absorb and apply knowledge from the
world around us, especially in rapidly acquiring new concepts from minimal examples …
world around us, especially in rapidly acquiring new concepts from minimal examples …