# Deep Metric Learning Assisted by Intra-variance in A Semi-supervised View of Learning

arXiv: https://arxiv.org/abs/2304.10941 · HTML: https://mlsanity.com/paper/2304.10941/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 51 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Deep Metric Learning via Lifted Structured Feature Embedding](https://mlsanity.com/paper/1511.06452/) (2015)
- [Circle Loss: A Unified Perspective of Pair Similarity Optimization](https://mlsanity.com/paper/2002.10857/) (2020)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [Sampling Matters in Deep Embedding Learning](https://mlsanity.com/paper/1706.07567/) (2017)
- [No Fuss Distance Metric Learning using Proxies](https://mlsanity.com/paper/1703.07464/) (2017)
- [Proxy Anchor Loss for Deep Metric Learning](https://mlsanity.com/paper/2003.13911/) (2020)
- [SoftTriple Loss: Deep Metric Learning Without Triplet Sampling](https://mlsanity.com/paper/1909.05235/) (2019)
- [Dataset Augmentation in Feature Space](https://mlsanity.com/paper/1702.05538/) (2017)
- [Deep Metric Learning with Hierarchical Triplet Loss](https://mlsanity.com/paper/1810.06951/) (2018)
- [Implicit Semantic Data Augmentation for Deep Networks](https://mlsanity.com/paper/1909.12220/) (2019)
- [Ranked List Loss for Deep Metric Learning](https://mlsanity.com/paper/1903.03238/) (2019)
- [Attention-based Ensemble for Deep Metric Learning](https://mlsanity.com/paper/1804.00382/) (2018)
- [Deep Metric Learning with BIER: Boosting Independent Embeddings Robustly](https://mlsanity.com/paper/1801.04815/) (2018)
- [Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies](https://mlsanity.com/paper/2010.13636/) (2020)
- [Good-Enough Example Extrapolation](https://mlsanity.com/paper/2109.05602/) (2021)
- [Improving Deep Metric Learning with Virtual Classes and Examples Mining](https://mlsanity.com/paper/2006.06611/) (2020)
- [Joint Debiased Representation Learning and Imbalanced Data Clustering](https://mlsanity.com/paper/2109.05232/) (2021)

## Abstract (arXiv)

Deep metric learning aims to construct an embedding space where samples of the same class are close to each other, while samples of different classes are far away from each other. Most existing deep metric learning methods attempt to maximize the difference of inter-class features. And semantic related information is obtained by increasing the distance between samples of different classes in the embedding space. However, compressing all positive samples together while creating large margins between different classes unconsciously destroys the local structure between similar samples. Ignoring the intra-class variance contained in the local structure between similar samples, the embedding space obtained from training receives lower generalizability over unseen classes, which would lead to the network overfitting the training set and crashing on the test set. To address these considerations, this paper designs a self-supervised generative assisted ranking framework that provides a semi-supervised view of intra-class variance learning scheme for typical supervised deep metric learning. Specifically, this paper performs sample synthesis with different intensities and diversity for samples satisfying certain conditions to simulate the complex transformation of intra-class samples. And an intra-class ranking loss function is designed using the idea of self-supervised learning to constrain the network to maintain the intra-class distribution during the training process to capture the subtle intra-class variance. With this approach, a more realistic embedding space can be obtained in which global and local structures of samples are well preserved, thus enhancing the effectiveness of downstream tasks. Extensive experiments on four benchmarks have shown that this approach surpasses state-of-the-art methods