Utilizing auxiliary outlier datasets to regularize the machine learning model has
demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the …

Diffusion models have demonstrated impressive performance in text-to-image generation.
They utilize a text encoder and cross-attention blocks to infuse textual information into …

Node outlier detection in attributed graphs is a challenging problem for which there is no
method that would work well across different datasets. Motivated by the state-of-the-art …

Detecting out-of-distribution (OOD) samples is a problem of practical importance for a
reliable use of deep neural networks (DNNs) in production settings. The corollary to this …

Image diffusion has recently shown remarkable performance in image synthesis and
implicitly as an image prior. Such a prior has been used with conditioning to solve the …

The out-of-distribution (OOD) detection task is crucial for the real-world deployment of
machine learning models. In this paper, we propose to study the problem from the …