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..
Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings
Authors: Jingchao Ni, Wei Cheng, Zhengzhang Chen, Takayoshi Asakura, Tomoya Soma, Sho Kato, Haifeng Chen
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark datasets and a real-life medical dataset indicate the effectiveness of our method. |
| Researcher Affiliation | Industry | Jingchao Ni1, Wei Cheng1, Zhengzhang Chen1, Takayoshi Asakura2, Tomoya Soma2, Sho Kato3, Haifeng Chen1 1NEC Laboratories America, 2NEC Corporation, 3Renascience, Inc. |
| Pseudocode | Yes | Algorithm 1: Superclass-conditional Gaussian mixture model (SCGM) |
| Open Source Code | Yes | The code of SCGM is available at https://github.com/nijingchao/SCGM for reproducibility study. |
| Open Datasets | Yes | The table below summarizes the benchmark datasets: (1) BREEDS (Santurkar et al., 2020) includes four datasets {Living17, Nonliving26, Entity13, Entity30} derived from Image Net with class hierarchy calibrated...; (2) CIFAR-100 (Krizhevsky, 2009); and (3) tiered Image Net (Ren et al., 2018) |
| Dataset Splits | Yes | For BREEDS and CIFAR-100, the val set is 10% of the train set. ...tiered Image Net... was divided into 20/6/8 splits for (disjoint) train/val/test sets. |
| Hardware Specification | Yes | Table 4: Computational costs on 4 Quadro RTX6000 24G GPUs. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as programming languages, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | For training, we used cosine annealing with warm restarts schedule (Loshchilov & Hutter, 2017) with 20 epochs per cycle. The batch size was 256 for BREEDS, 1024 for CIFAR-100, and 512 for tiered Image Net. The learning rate was 0.03 for BREEDs, and 0.12 for CIFAR-100 and tiered Image Net. The weight decay was 1e 4. All models were trained with 200 epochs. ... For SCGM, we set γ = 0.5, σ2 = 0.1, and λ = 25 (λ follows (Asano et al., 2020)). |