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..
Learning to Learn By Self-Critique
Authors: Antreas Antoniou, Amos J. Storkey
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that SCA offers substantially reduced errorrates compared to baselines which only adapt on the support-set, and results in state of the art benchmark performance on Mini-Image Net and Caltech-UCSD Birds 200. |
| Researcher Affiliation | Academia | Antreas Antoniou University of Edinburgh {a.antoniou}@sms.ed.ac.uk Amos Storkey University of Edinburgh {a.storkey}@ed.ac.uk |
| Pseudocode | Yes | Algorithm 1 SCA Algorithm combined with MAML |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code related to the described methodology. |
| Open Datasets | Yes | We evaluate the proposed method on the established few-shot learning benchmarks of Mini Image Net (Ravi and Larochelle, 2016) and Caltech-UCSD Birds 200 (CUB) (Chen et al., 2019). |
| Dataset Splits | Yes | The dataset is split into 3 subsets beforehand, the meta-training, meta-validation and the meta-test sets, used for training, validation and testing respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (like exact GPU/CPU models or processor types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | The paper describes specific experimental setup details such as network architecture, regularization parameters (dropout probability of 0.5, weight decay rate 2e-05), and training steps (five SGD steps, 1 step for critic update). For example, 'The Dense Net is reqularised using dropout after each block (with drop probability of 0.5) and weight decay (with decay rate 2e-05).' |