# Deep Features Analysis with Attention Networks Source: [Deep Features Analysis with Attention Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:23:52.793Z Authors: Shipeng Xie, Da Chen, Rong Zhang, Hui Xue Published: 2019-01-20T18:44:43.000Z ## Abstract Deep neural network models have recently draw lots of attention, as it consistently produce impressive results in many computer vision tasks such as image classification, object detection, etc. However, interpreting such model and show the reason why it performs quite well becomes a challenging question. In this paper, we propose a novel method to interpret the neural network models with attention mechanism. Inspired by the heatmap visualization, we analyze the relation between classification accuracy with the attention based heatmap. An improved attention based method is also included and illustrate that a better classifier can be interpreted by the attention based heatmap. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()