Pith. sign in

REVIEW 6 cited by

PyTorch Metric Learning

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2008.09164 v1 pith:25R4PMGG submitted 2020-08-20 cs.CV cs.LG

Kevin Musgrave , Serge Belongie , Ser-Nam Lim This is my paper · ORCID
classification cs.CVcs.LG
keywords algorithmslearningmetriccodepytorchusersaimsallows
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Deep metric learning algorithms have a wide variety of applications, but implementing these algorithms can be tedious and time consuming. PyTorch Metric Learning is an open source library that aims to remove this barrier for both researchers and practitioners. The modular and flexible design allows users to easily try out different combinations of algorithms in their existing code. It also comes with complete train/test workflows, for users who want results fast. Code and documentation is available at https://www.github.com/KevinMusgrave/pytorch-metric-learning.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AstroLoc: Robust Space to Ground Image Localizer

    cs.CV 2025-02 conditional novelty 6.0 of 10

    AstroLoc trains an astronaut-to-satellite image retrieval model using 221k automatically footprinted astronaut photos, achieving state-of-the-art recall on APL benchmarks and related space-to-ground tasks.

  2. On dataset transferability in medical image classification

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A new metric combining NCA-based feature separability with a conv2/conv1 gradient ratio ranks medical image source models better than six existing metrics on most targets, though statistical significance is limited.

  3. CLERF: Contrastive LEaRning for Full Range Head Pose Estimation

    cs.CV 2024-12 conditional novelty 6.0 of 10

    CLERF uses contrastive learning with GAN-generated same-pose pairs and geometric augmentation to achieve full-range, including upside-down, head pose estimation, matching or beating prior models on standard and transf...

  4. Face and Voice Cross-modal Association with Learning Convex Feature Embedding

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Convex combinations of attention-weighted face and voice embeddings, trained with clustering and multi-similarity loss, improve cross-modal person association on VoxCeleb.

  5. Cross-Domain Generalization in Optical Networks via Joint Contrastive and Classification Learning

    cs.NI 2026-07 conditional novelty 4.0 of 10

    Joint contrastive and classification training improves worst-case cross-domain QoT estimation and few-shot adaptation on three public optical-network datasets, at the cost of peak performance in easy transfers.

  6. YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID

    cs.CV 2025-01 conditional novelty 3.0 of 10

    A lightweight YOLO11-based tracker learns appearance embeddings without identity labels and reports competitive MOT17/MOT20 tracking accuracy at high speed.

Pith tools