#out-of-distribution detection
10 papers · 1 filter
Uncertainty quantification for trustworthy deep learning: Methods and measures
H. Martin Gillis, Thomas Trappenberg
The paper surveys methods for quantifying uncertainty in deep neural networks, focusing on ensemble-based and approximate Bayesian approaches and how their outputs are measured.
Representation Trajectories Matters: Complementary Evidence for OOD Detection and Image Classification
Ignacio M. De la Jara, Cristian Rodriguez-Opazo, Hamed Damirchi +2
The paper investigates how the step‑by‑step changes in a vision model’s internal representations (representation trajectories) can be used to improve out‑of‑distribution detection…
Spend Experts Where You Are Unsure: Confidence-Adaptive Routing for Mixture-of-Experts LoRA
Tom Saliencro, Rohan Desai, Priya Nair +2
The paper introduces CARE, a confidence-adaptive routing method for Mixture-of-Experts LoRA that dynamically selects the number of experts per token based on the router’s uncertain…
Towards Out-of-Distribution Detection in Vocoder Recognition via Latent Feature Reconstruction
Renmingyue Du, Jixun Yao, Qiuqiang Kong +1
The paper proposes a reconstruction‑based method using autoencoders to detect out‑of‑distribution vocoder samples by reconstructing WavLM acoustic features, with contrastive learni…
The Hyperspherical Geometry of CLIP Latent Space: A Semantic Mixture Model
Zijie Yu, Gaowen Liu, Ramana Rao Kompella +2
The paper introduces a probabilistic model for CLIP embeddings using mixtures of von Mises-Fisher distributions on the unit hypersphere, improving density estimation and detection…
Variational Inference for Evidential Deep Learning
Jiawei Tang, Xinyan Du, Hui Liu +2
The paper introduces VI-EDL, a variational inference framework for evidential deep learning that controls evidence growth and provides theoretical guarantees for uncertainty estima…