4 accepted papers
Computer vision models can be explained by attributing the output decision to the input pixels. While effective methods for explaining convolutional neural networks have been proposed, these methods often produce low-quality attributions when applied to vision transformers (ViTs). State-of-the-art m…
Attribution algorithms explain computer vision models by attributing the model response to pixels within the input. Existing attribution methods generate explanations by combining transformations of internal model representations such as class activation maps, gradients, attention, or relevance scor…
Attribution algorithms play an instrumental role in human interpretation of AI models. The methods measure the importance of the input features to the model output decision, which can be displayed as an attribution map for image classifiers. Perturbation tests are the state-of-the-art approach to ev…
Attribution algorithms are frequently employed to explain the decisions of neural network models. Integrated Gradients (IG) is an influential attribution method due to its strong axiomatic foundation. The algorithm is based on integrating the gradients along a path from a reference image to the inpu…