Self-taught Metric Learning Without Labels

Abstract

We present a novel self-taught framework for unsupervised metric learning, which alternates between predicting class-equivalence relations between data through a moving average of an embedding model and learning the model with the predicted relations as pseudo labels. At the heart of our framework lies an algorithm that investigates contexts of data on the embedding space to predict their class-equivalence relations as pseudo labels. The algorithm enables efficient end-to-end training since it demands no off-the-shelf module for pseudo labeling. Also, the class-equivalence relations provide rich supervisory signals for learning an embedding space. On standard benchmarks for metric learning, it clearly outperforms existing unsupervised learning methods and sometimes even beats supervised learning models using the same backbone network. It is also applied to semi-supervised metric learning as a way of exploiting additional unlabeled data, and achieves the state of the art by boosting per

Stats

• citations17
• S2 citations—
• github stars0
• HF likes0
• heat score9.41
• arxiv keykim2022self

Related papers

• SLADE: A Self-training Framework For Distance Metric Learning (2020)6.77
• Semi-supervised Deep Learning By Metric Embedding (2016)0.00
• Deep Metric Learning Assisted By Intra-variance In A Semi-supervised View Of Learning (2023)5.24
• A Probabilistic Approach For Learning Embeddings Without Supervision (2019)0.00
• Self-supervised Metric Learning In Multi-view Data: A Downstream Task Perspective (2021)3.58
• Deep Metric Learning Beyond Binary Supervision (2019)14.39
• Learning To Learn In A Semi-supervised Fashion (2020)2.26
• Deep Metric Learning Meets Deep Clustering: An Novel Unsupervised Approach For Feature Embedding (2020)2.26