arXiv:2608. 19407v1 Announce Type: cross Abstract: Deep Learning models can include billions of parameters or more, making it difficult to explain their internal transformations and outputs.
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arXiv:2411. 05698v3 Announce Type: replace-cross Abstract: Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification.
arXiv:2507. 09092v2 Announce Type: replace-cross Abstract: With the intervention of machine vision in our crucial day to day necessities including healthcare and automated power plants, attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network provides specific inferences.
arXiv:2608. 12299v1 Announce Type: cross Abstract: Class activation mapping (CAM) is one of the most widely used visual explanation families in explainable artificial intelligence.
MIMONet is a saliency detection model that uses multi‑scale inputs and outputs to better handle objects of varying sizes. It processes three differently sized images through separate encoder branches that exchange information, allowing each branch to learn size‑variation knowledge from the others. A Multi‑scale Perception module further refines features, and a Joint Saliency Loss ensures consistent, well‑preserved boundaries across the multiple saliency maps produced.
arXiv:2511. 12723v2 Announce Type: replace Abstract: Deep neural networks typically rely on the representation produced by their final hidden layer to make predictions, implicitly assuming that this single vector fully captures the semantics encoded across all preceding transformations.
arXiv:2606. 25606v1 Announce Type: cross Abstract: Given the widespread prevalence of depression and its consequential impact on individuals and society, it is crucial to obtain objective measures for early diagnosis and intervention.