# Guided Zoom: Questioning Network Evidence for Fine-grained Classification

arXiv: https://arxiv.org/abs/1812.02626 · HTML: https://mlsanity.com/paper/1812.02626/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2018.

## Built on by (most cited first, 8 of 8)

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- [XAI-Enhanced Semantic Segmentation Models for Visual Quality Inspection](https://mlsanity.com/paper/2401.09900/) (2024)

## Abstract (arXiv)

We propose Guided Zoom, an approach that utilizes spatial grounding of a model's decision to make more informed predictions. It does so by making sure the model has "the right reasons" for a prediction, defined as reasons that are coherent with those used to make similar correct decisions at training time. The reason/evidence upon which a deep convolutional neural network makes a prediction is defined to be the spatial grounding, in the pixel space, for a specific class conditional probability in the model output. Guided Zoom examines how reasonable such evidence is for each of the top-k predicted classes, rather than solely trusting the top-1 prediction. We show that Guided Zoom improves the classification accuracy of a deep convolutional neural network model and obtains state-of-the-art results on three fine-grained classification benchmark datasets.