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..
Adversarial Task Up-sampling for Meta-learning
Authors: Yichen WU, Long-Kai Huang, Ying Wei
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On fewshot sine regression and image classification datasets, we empirically validate the marked improvement of ATU over state-of-the-art task augmentation strategies in the meta-testing performance and also the quality of up-sampled tasks. |
| Researcher Affiliation | Collaboration | Yichen Wu1,2 , Long-Kai Huang2 , Ying Wei1 1City University of Hong Kong, 2Tencent AI Lab |
| Pseudocode | Yes | We summarize the proposed ATU in Algorithm 1 in Appendix A. |
| Open Source Code | No | We exploit the open-source datasets and will release the code. |
| Open Datasets | Yes | We consider four datasets (base classes number): mini Imagenet-S (12), ISIC [18] (4), Dermnet-S (30), and Tabular Murris [5] (57) covering classification tasks on general natural images, medical images, and gene data. Note that the mini Imagenet-S and Dermnet-S are constructed by limiting the base classes of mini Imangenet [33] and Dermnet [1], respectively. |
| Dataset Splits | Yes | In the meta-training phase, each task contains K support and K target (K=10) examples. We adopt mean squared error (MSE) as the loss function. For the base model fθ, we adopt a small neural network, which consists of an input layer of size 1, 2 hidden layers of size 40 with Re LU and an output layer of size 1. We use one gradient update with a fixed step size α=0.01 in inner loop, and use Adam as the outer-loop optimizer following [9, 15]. Moreover, the meta-learner is trained on 240,000 tasks with meta batch-size being 4. |
| Hardware Specification | Yes | We use a workstation with Intel i7-2600 CPUs and NVIDIA GeForce RTX 2080 Ti GPUs. The training of the models takes roughly 2-3 days. |
| Software Dependencies | No | The paper mentions using 'Adam as the outer-loop optimizer' but does not specify software components with version numbers (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | For the base model fθ, we adopt a small neural network, which consists of an input layer of size 1, 2 hidden layers of size 40 with Re LU and an output layer of size 1. We use one gradient update with a fixed step size α=0.01 in inner loop, and use Adam as the outer-loop optimizer following [9, 15]. Moreover, the meta-learner is trained on 240,000 tasks with meta batch-size being 4. |