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..
Understanding Self-Training for Gradual Domain Adaptation
Authors: Ananya Kumar, Tengyu Ma, Percy Liang
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Leveraging the gradual shift structure leads to higher accuracies on a rotating MNIST dataset, a forest Cover Type dataset, and a realistic Portraits dataset. We run experiments on three datasets (see Appendix C for more details): Rotating MNIST:..., Cover Type:..., Portraits:... |
| Researcher Affiliation | Academia | Ananya Kumar 1 Tengyu Ma 1 Percy Liang 1 1Stanford University, Stanford, California, USA. Correspondence to: Ananya Kumar <EMAIL>. |
| Pseudocode | No | The paper describes the algorithm using equations and descriptive text but does not include a formally structured pseudocode or algorithm block. |
| Open Source Code | Yes | All code, data, and experiments can be found on Coda Lab at https: //bit.ly/gradual-shift-codalab, code is also on Git Hub at https://github.com/p-lambda/ gradual_domain_adaptation. |
| Open Datasets | Yes | Rotating MNIST: Rotating MNIST is a semi-synthetic dataset where we rotate each MNIST image by an angle between 0 and 60 degrees. Cover Type: A dataset from the UCI repository where the goal is to predict the forest cover type at a particular location given 54 features (Blackard and Dean, 1999). Portraits: A real dataset comprising photos of high school seniors across years (Ginosar et al., 2017). |
| Dataset Splits | Yes | We split the 50,000 MNIST training set images into a source domain (images rotated between 0 and 5 degrees), intermediate domain (rotations between 5 and 60 degrees), and a target domain (rotations between 55 degrees and 60 degrees). |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions software components like 'neural networks' and 'logistic regression', but does not provide specific version numbers for any libraries, frameworks, or programming languages used in the experiments. |
| Experiment Setup | Yes | For rotating MNIST and Portraits we used a 3-layer convolutional network with dropout(0.5) and batchnorm on the last layer... For the Cover Type dataset we used a 2 hidden layer feedforward neural network with dropout(0.5) and batchnorm on the last layer... For each step of self-training, we filter out the 10% of images where the model s prediction was least confident |