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 | Conference PDF | Archive PDF | Plain Text | 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. 1{zhangyiz,yanliu.cs}@usc.edu; 2nijingchao@gmail.com; 3{weicheng,zchen,haifeng}@nec-labs.com; 4ltong@stellarcyber.ai |
| 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]. |