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..
Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning
Authors: Yizhou Zhang, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Liang Tong, Haifeng Chen, Yan Liu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark datasets indicate the effectiveness of our method for both sample classification and novel task detection. |
| Researcher Affiliation | Collaboration | Yizhou Zhang1 , Jingchao Ni2 , Wei Cheng3, Zhengzhang Chen3, Liang Tong4 , Haifeng Chen3, Yan Liu1 1University of Southern California 2AWS AI Labs 3NEC Laboratories America 4Stellar Cyber Inc. 1EMAIL; EMAIL; 3EMAIL; EMAIL |
| Pseudocode | Yes | Algorithm 1: Hierarchical Gaussian Mixture based Task Generative Model (HTGM) |
| Open Source Code | No | The paper does not provide an explicit statement about open-source code release or a link to a code repository. |
| Open Datasets | Yes | The first dataset is the Plain-Multi benchmark [52]. It includes four fine-grained image classification datasets, i.e., CUB-200-2011 (Bird), Describable Textures Dataset (Texture), FGVC of Aircraft (Aircraft), and FGVCx-Fungi (Fungi). The second dataset is the Art-Multi benchmark [53]...Moreover, we used the Mini-Image Net dataset [47] to evaluate the case of uni-component distribution of tasks, which is discussed in Appendix D.6. |
| Dataset Splits | Yes | Both benchmarks were divided into the meta-training, meta-validation, and meta-test sets by following their corresponding papers. |
| Hardware Specification | Yes | We evaluated and trained all of the models on RTX 6000 GPU with 24 GB memory. |
| Software Dependencies | No | The paper mentions "Adam optimizer" and "Res Net-12" (implying common deep learning frameworks like PyTorch or TensorFlow), but it does not specify versions for any key software components or libraries required to replicate the experiments. |
| Experiment Setup | Yes | For training, Adam optimizer was used. Each batch contains 4 tasks. Each model was trained with 20000 episodes. The learning rate of the metric-based methods was 1e 3. The learning rates for the inner- and outer-loops of the optimization-based methods were 1e 3 and 1e 4. The weight decay was 1e 4. For HTGM, we set σ = 1.0, σ = 0.1, α = 0.5 (0.9) for 1-shot (5-shot) tasks. The number of mixture components r varies w.r.t. different datasets, and was grid-searched within [2, 4, 8, 16, 32]. |