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..
Deep Metric Learning with Spherical Embedding
Authors: Dingyi Zhang, Yingming Li, Zhongfei Zhang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on deep metric learning, face recognition, and contrastive self-supervised learning show that the SEC-based angular space learning strategy significantly improves the performance of the state-of-the-art. |
| Researcher Affiliation | Academia | Dingyi Zhang1, Yingming Li1 , Zhongfei Zhang2 1College of Information Science & Electronic Engineering, Zhejiang University, China 2Department of Computer Science, Binghamton University, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it state that code is available. |
| Open Datasets | Yes | (1) Deep metric learning task: we employ four fine-grained image clustering and retrieval benchmarks, including CUB200-2011 [28], Cars196 [29], SOP [5], and In-Shop [30]... (2) Face recognition task: CASIA-Web Face [32] is employed as the training set while the testing sets include LFW [33], Age DB30 [34], CFP-FP [35], and Mega Face Challenge 1 [36]... (3) Contrastive self-supervised learning task: we follow the framework and settings in SimCLR [19] and evaluate on CIFAR-10 and CIFAR-100 datasets [38]. |
| Dataset Splits | Yes | We Follow the protocol in [5, 30] to split the training and testing sets for them as in Table 1. For CUB200-2011 and Cars196, we do not use the bounding box annotations during training and testing. (Table 1 includes 'Train Test' sample counts). CASIA-Web Face [32] is employed as the training set while the testing sets include LFW [33], Age DB30 [34], CFP-FP [35], and Mega Face Challenge 1 [36]. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer [27]', 'SGD with momentum 0.9', 'BN-Inception [13]', 'ResNet50 [37]', 'SimCLR [19]', and 'NT-Xent' loss, but does not specify any version numbers for these software dependencies. |
| Experiment Setup | Yes | We set batch size to 120 and embedding size to 512 for all methods and datasets. We use Adam optimizer [27]. The compared methods are vanilla triplet loss (m = 1.0), semihard triplet loss (m = 0.2) [4], normalized N-pair loss (s = 25) [10, 18], and multi-similarity loss (ϵ = 0.1, λ = 0.5, α = 2, β = 40) [11]... We set batch size to 256 and embedding size to 512 for all methods. We use SGD with momentum 0.9. The hyper-parameter s is set to 64 while m for sphereface, cosface, and arcface are 3, 0.35, and 0.45, respectively... NT-Xent with temperature 0.5 is the loss and the batch size is 256. |