GAIA Gradient-Based Attribution for OOD Detection

Deep neural networks (DNNs) have shown incredible accuracy across numerous applications. However, their inability to handle out-of-distribution (OOD) samples can lead to unpredictable and potentially unsafe behavior. This post explores the recent paper on the Gradient Abnormality Inspection and Aggregation (GAIA)(Chen et al., 2023) framework, which introduces an innovative approach to enhance OOD detection.

Gradient-aware...

BACS - Tackling Background Ambiguity in Continual Semantic Segmentation

Semantic Segmentation, the task of assigning a class label to every pixel in an image, is fundamental for detailed scene understanding, especially in applications like autonomous driving and robotics. However, creating these pixel-perfect annotations is laborious. Furthermore, real-world systems often need to learn...

Survey on Uncertainty Estimation in Deep Learning

A distinction between aleatoric and epistemic uncertainties is proposed in the domain of medical decision-making (Senge et al., 2014). Their paper explained that aleatoric and epistemic uncertainties are not distinguished in Bayesian inference. Moreover, the expectation over the model with respect to the posterior is used to get our prediction leading to an averaged epistemic...