Strength from Weakness: Fast Learning Using Weak Supervision
Authors: Joshua Robinson, Stefanie Jegelka, Suvrit Sra
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical results are reflected empirically across a range of tasks and illustrate how weak labels speed up learning on the strong task. |
| Researcher Affiliation | Academia | 1Massachusetts Institute of Technology, Cambridge, MA 02139. Correspondence to: Joshua Robinson <joshrob@mit.edu>. |
| Pseudocode | Yes | Algorithm 1 Pretrain-finetune meta-algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | Yes | For our CIFAR-10 experiments...fine tuning on a small subset CIFAR-100...Databases left to right: MNIST, SVHN, and CIFAR-10. ...TREC fast-based question categorization dataset. |
| Dataset Splits | No | The paper describes a 'held out dataset' for training an auxiliary network and discusses 'generalization error', but it does not specify concrete train/validation/test split percentages or sample counts for its experiments. |
| Hardware Specification | No | The paper mentions that 'All image-based experiments use either a Res Net-18 or Res Net-34 for the weak feature map g (see Appendix C for full details)', suggesting hardware details might be in the appendix, but it does not provide specific hardware models (e.g., GPU, CPU) in the provided text. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. While PyTorch is referenced, its version is not stated as a dependency for the authors' work. |
| Experiment Setup | No | The paper states 'All image-based experiments use either a Res Net-18 or Res Net-34 for the weak feature map g (see Appendix C for full details)', implying experimental setup details are in the appendix. However, no specific hyperparameters or training configurations are provided in the main text. |