Out-of-Distribution Detection Ensuring AI Robustness

Deep neural networks can solve various complex tasks and achieve state-of-the-art results in multiple domains such as image classification, speech recognition, machine translation, robotics, and control. However, due to the distributional shift between collected training data and actual test data, The trained neural network has a difference between the network’s performance on the training and unseen real...

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...