# Explainable Metric Learning for Deflating Data Bias

arXiv: https://arxiv.org/abs/2407.04866 · HTML: https://mlsanity.com/paper/2407.04866/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 29 references that are in the map.
- First posted on arXiv 5 Jul 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [Proxy Anchor Loss for Deep Metric Learning](https://mlsanity.com/paper/2003.13911/) (2020)
- [XRAI: Better Attributions Through Regions](https://mlsanity.com/paper/1906.02825/) (2019)
- [Deep Metric Learning with Angular Loss](https://mlsanity.com/paper/1708.01682/) (2017)
- [Visualizing Deep Networks by Optimizing with Integrated Gradients](https://mlsanity.com/paper/1905.00954/) (2019)
- [PyTorch Metric Learning](https://mlsanity.com/paper/2008.09164/) (2020)
- [Deep Human Parsing with Active Template Regression](https://mlsanity.com/paper/1503.02391/) (2015)
- [Signal-to-Noise Ratio: A Robust Distance Metric for Deep Metric Learning](https://mlsanity.com/paper/1904.02616/) (2019)
- [Towards Interpretable Deep Metric Learning with Structural Matching](https://mlsanity.com/paper/2108.05889/) (2021)
- [Attributable Visual Similarity Learning](https://mlsanity.com/paper/2203.14932/) (2022)
- [Adversarial Metric Learning](https://mlsanity.com/paper/1802.03170/) (2018)

## Abstract (arXiv)

Image classification is an essential part of computer vision which assigns a given input image to a specific category based on the similarity evaluation within given criteria. While promising classifiers can be obtained through deep learning models, these approaches lack explainability, where the classification results are hard to interpret in a human-understandable way. In this paper, we present an explainable metric learning framework, which constructs hierarchical levels of semantic segments of an image for better interpretability. The key methodology involves a bottom-up learning strategy, starting by training the local metric learning model for the individual segments and then combining segments to compose comprehensive metrics in a tree. Specifically, our approach enables a more human-understandable similarity measurement between two images based on the semantic segments within it, which can be utilized to generate new samples to reduce bias in a training dataset. Extensive experimental evaluation demonstrates that the proposed approach can drastically improve model accuracy compared with state-of-the-art methods.