Learning to Self-Train for Semi-Supervised Few-Shot Classification
Authors: Xinzhe Li, Qianru Sun, Yaoyao Liu, Qin Zhou, Shibao Zheng, Tat-Seng Chua, Bernt Schiele
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed LST method in terms of few-shot image classification accuracy in semisupervised settings. Below we describe the two benchmarks we evaluate on, details of settings, comparisons to state-of-the-art methods, and an ablation study. |
| Researcher Affiliation | Collaboration | Xinzhe Li1 Qianru Sun2 Yaoyao Liu3 Shibao Zheng1 Qin Zhou4 Tat-Seng Chua5 Bernt Schiele6 1Shanghai Jiao Tong University 2Singapore Management University 3Tianjin University 4Alibaba Group 5National University of Singapore 6Max Planck Institute for Informatics, Saarland Informatics Campus |
| Pseudocode | No | The paper includes diagrams illustrating the method's pipeline and training procedures (Figure 1, Figure 2), but these are not structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is at github.com/xinzheli1217/learning-to-self-train. |
| Open Datasets | Yes | We conduct our experiments on two subsets of Image Net [26]. mini Image Net was firstly proposed by Vinyals et al. [36] ... tiered Image Net was proposed by Ren et al. [24]. |
| Dataset Splits | Yes | In the uniform setting, these classes are divided into 64, 16, and 20 respectively for meta-train, meta-validation, and meta-test. |
| Hardware Specification | No | The paper describes network architectures (e.g., Res Net-12) and general computational processes but does not provide specific hardware details (e.g., exact GPU/CPU models) used for running its experiments. |
| Software Dependencies | No | The paper mentions common deep learning frameworks and libraries implicitly, but it does not specify any software components with their version numbers required for reproducibility. |
| Experiment Setup | Yes | Base-learning rate α (in Eq. 1, Eq. 4 and Eq. 6) is set to 0.01. Meta-learning rates β1 and β2 (in Eq. 7 and Eq. 8) are set to 0.001 initially and decay to the half value every 1k meta iterations until a minimum value 0.0001 is reached. We use a meta-batch size of 2 and run 15k meta iterations. In recursive training, we use 6 (3) recursive stages for 1-shot (5-shot) tasks. Each recursive stage contains 10 re-training and 30 fine-tuning steps. |