3 papers
cs.LG2025
Prompt Optimization Meets Subspace Representation Learning for Few-shot Out-of-Distribution Detection
Faizul Rakib Sayem, Shahana Ibrahim
The reliability of artificial intelligence (AI) systems in open-world settings depends heavily on their ability to flag out-of-distribution (OOD) inputs unseen during training. Rec…
cs.LG2025
Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition
Tarhib Al Azad, Shahana Ibrahim
Robust out-of-distribution (OOD) detection is an indispensable component of modern artificial intelligence (AI) systems, especially in safety-critical applications where models mus…
cs.LG2025
Pseudo-label Induced Subspace Representation Learning for Robust Out-of-Distribution Detection
Tarhib Al Azad, Faizul Rakib Sayem, Shahana Ibrahim
Out-of-distribution (OOD) detection lies at the heart of robust artificial intelligence (AI), aiming to identify samples from novel distributions beyond the training set. Recent ap…