‘Less Than One’-Shot Learning: Learning N Classes From M < N Samples
Authors: Ilia Sucholutsky,Matthias Schonlau9739-9746
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We both theoretically and empirically demonstrate feasibility. We also perform a case study to confirm that soft labels can be used to represent training sets using fewer prototypes than there are classes, achieving large increases in sample-efficiency over regular (hard-label) prototypes. |
| Researcher Affiliation | Academia | Ilia Sucholutsky, Matthias Schonlau Department of Statistics and Actuarial Science University of Waterloo Waterloo, Ontario, Canada isucholu@uwaterloo.ca |
| Pseudocode | No | The paper defines 'Definition 4: The distance-weighted soft-label prototype k Nearest Neighbors (SLa Pk NN) classification rule' with formal notation, but not in a pseudocode block format. |
| Open Source Code | Yes | Our code, the appendix, and a live web demo can be found at https://github.com/ilia10000/SLkNN |
| Open Datasets | Yes | Sucholutsky and Schonlau (2019) showed that it is possible to design a set of five soft-labelled synthetic images, sometimes called prototypes , that train neural networks to over 90% accuracy on the ten-class MNIST task. |
| Dataset Splits | No | The paper mentions the MNIST dataset but does not provide specific details on training, validation, or test splits. It focuses on the theoretical aspects of class separation rather than empirical model training specifics. |
| Hardware Specification | No | No specific hardware specifications (e.g., GPU/CPU models, memory) used for experiments were mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned in the paper. |
| Experiment Setup | No | The paper focuses on theoretical derivations and conceptual models (SLa Pk NN) rather than providing specific hyperparameter values or detailed training configurations for empirical experiments. |