Understanding Self-Training for Gradual Domain Adaptation
Authors: Ananya Kumar, Tengyu Ma, Percy Liang
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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 <ananya@cs.stanford.edu>. |
| 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 |