Learning to Learn By Self-Critique
Authors: Antreas Antoniou, Amos J. Storkey
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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).' |