Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning
Authors: Geonmo Gu, Byungsoo Ko, Han-Gyu Kim1460-1468
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on four famous benchmarks in image retrieval tasks demonstrate that Proxy Synthesis significantly boosts the performance of proxy-based losses and achieves state-of-the-art performance. |
| Researcher Affiliation | Industry | Geonmo Gu 1, Byungsoo Ko 1, Han-Gyu Kim 2 1 NAVER/LINE Vision, 2 NAVER Clova Speech EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes the method in prose but does not provide structured pseudocode or algorithm blocks in the main text. |
| Open Source Code | Yes | Our implementation is available at github.com/navervision/proxy-synthesis. |
| Open Datasets | Yes | We evaluate the proposed method with respect to four benchmarks in metric learning: CUB-200-2011 (CUB200) (Wah et al. 2011), CARS196 (Krause et al. 2013), Standford Online Products (SOP) (Oh Song et al. 2016), and In-Shop Clothes (In-Shop) (Liu et al. 2016). |
| Dataset Splits | Yes | We include an evaluation procedure designed from work A metric learning reality check (Musgrave, Belongie, and Lim 2020) and call it MLRC evaluation, which contains 4-fold cross-validation, ensemble evaluation, and usage of fair metrics (P@1, RP, and MAP@R). |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU, CPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions using an 'Inception network' and provides a GitHub link, but does not specify particular software dependencies with version numbers (e.g., PyTorch 1.9, Python 3.8). |
| Experiment Setup | Yes | Experiments are performed on an Inception network with batch normalization (Ioffe and Szegedy 2015) with a 512 embedding dimension. For the hyper-parameters of Proxy Synthesis, α and µ are set to 0.4 and 1.0, respectively. |