Context-Aware Interpretable Representations for Retrieval and Graph Convolutional Network Classification
The advances in visual information modeling and representation during the last decades are remarkable, mainly supported by Convolutional Neural Networks, Transformer-based, and Foundation Models. Despite this progress, critical challenges regarding the nature of similarity assessment and model transparency have been neglected. A primary concern is the Geometric Gap, where traditional pairwise measures fail to capture the intrinsic geometry of the dataset manifold. Furthermore, the Interpretability Gap persists, as representations often lack alignment with human cognition. Therefore, how to provide interpretability to representations while maintaining low dimensionality and high effectiven...
| Authors | Thiago César Castilho Almeida, Gustavo Rosseto Letício, Vinicius Atsushi Sato Kawai et al. |
|---|---|
| Primary category | cs.LG |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.