Structured Prediction for Conditional Meta-Learning
Authors: Ruohan Wang, Yiannis Demiris, Carlo Ciliberto
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that TASML improves the performance of existing meta-learning models, and outperforms the state-of-the-art on benchmark datasets. We empirically evaluate TASML on several competitive few-shot classification benchmarks, including datasets derived from IMAGENET and CIFAR respectively. |
| Researcher Affiliation | Academia | Ruohan Wang, Yiannis Demiris, Carlo Ciliberto Dept. of Electrical and Electronic Engineering Imperial College London London, UK {r.wang16,y.demiris,c.ciliberto}@imperial.ac.uk |
| Pseudocode | Yes | Algorithm 1 TASML |
| Open Source Code | Yes | TASML implementation is available at https://github.com/RuohanW/Tasml |
| Open Datasets | Yes | We empirically evaluate TASML on several competitive few-shot classification benchmarks, including datasets derived from IMAGENET and CIFAR respectively. We perform experiments1 on C-way-K-shot learning within the episodic formulation of [53]. ... We evaluate the proposed method against a wide range of meta-learning algorithms on three few-shot learning benchmarks: the mini IMAGENET, tiered IMAGENET and CIFAR-FS datasets. |
| Dataset Splits | Yes | For training, validation and testing, we sample three separate meta-datasets Str, Sval and Sts, each accessing a disjoint set of classes (e.g. no class in Sts appears in Str or Sval). Dval contains samples from the same C classes for estimating model generalization and training meta-learner. |
| Hardware Specification | Yes | Tab. 4, which reports the average number of meta-gradient steps per second on a single Nvidia GTX 2080. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies (e.g., libraries, frameworks like PyTorch). |
| Experiment Setup | Yes | We consider the commonly used 5-way-1-shot and 5-way-5-shot settings. In our experiments we chose M to be 1% of N. Appendix B reports further experimental details including network specification and hyperparameter choice. |