Topology of Out-of-Distribution Examples in Deep Neural Networks

As deep neural networks (DNNs) become more common, concerns about their robustness, particularly when facing unfamiliar inputs, are growing. These models often exhibit overconfidence when making incorrect predictions on out-of-distribution (OOD) examples. This...

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...