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 [1].
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. |