Task Cooperation for Semi-Supervised Few-Shot Learning
Authors: Han-Jia Ye, Xin-Chun Li, De-Chuan Zhan10682-10690
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The state-of-the-art few-shot classification results on Mini Image Net and Tiered Image Net verify the superiority of TACO to leverage unlabeled data and task relationship in meta-learning. |
| Researcher Affiliation | Academia | Han-Jia Ye, Xin-Chun Li, De-Chuan Zhan State Key Laboratory for Novel Software Technology, Nanjing University {yehj, lixc, zhandc}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 The meta-training flow of the TACO. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | Datasets. Mini Image Net (Vinyals et al. 2016) and Tiered Image Net (Ren et al. 2018) contain 100 classes and 608 classes respectively. |
| Dataset Splits | Yes | The first strategy splits all examples in the meta-train set across classes (SAC). In this case, we randomly select 30% classes in the meta-train set as the labeled part and uses the instances in the remaining classes without their labels as the unlabeled set. Similarly, we randomly select 30% instances across instances (SAI). In the SAI case, it is possible to sample non-distractor classes from the unlabeled pool, which reduces the classification difficulty w.r.t. SAC to some extent. ... Thus instead of preserving the whole meta-val set, we adopt the same SAC or SAI split methods to reduce the size of the meta-val set. |
| Hardware Specification | No | The paper does not specify any particular hardware components such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions '4-layer Conv Net' and 'Res Net-12' as backbones and 'Proto Net' for implementation, but does not provide specific version numbers for any software dependencies like programming languages, libraries, or frameworks. |
| Experiment Setup | Yes | Implementation Details. We use a 4-layer Conv Net (Vinyals et al. 2016; Finn, Abbeel, and Levine 2017; Snell, Swersky, and Zemel 2017) as the backbone... For semi-supervised FSL, we sample 75 unlabeled instances in each mini-batch. |