Sinkhorn Label Allocation: Semi-Supervised Classification via Annealed Self-Training
ICML 2021
Overview
Semi-supervised learning (SSL) is the setting where the learner is given access to a collection of unlabeled data in addition to a labeled dataset. The goal is to learn a more accurate predictor than would otherwise be possible using the labeled data alone. Self-training is a standard approach to SSL where the learner's own predictions on unlabeled data are used as supervision during training. As one may expect, the success of self-training depends crucially on the label assignment step: if too many unlabeled examples are incorrectly labeled, we may end up in a situation where prediction errors compound over the course of training, ultimately resulting in a poor predictor. Consequently, practitioners have developed a wide range of label assignment heuristics which serve to mitigate the label noise introduced through the self-training process. For example, a commonly seen heuristic is to assign a label only if the current predictor's confidence exceeds a certain threshold.
In our paper, we reframe the label assignment process in self-training as an optimization problem which aims to find a minimum cost matching between unlabeled examples and classes, subject to a set of constraints. As it turns out, this formulation is sufficiently flexible to subsume a variety of popular label assignment heuristics, e.g., confidence thresholding, label annealing, class balancing, and others. At the same time, the particular form of the optimization problem admits an efficient approximation algorithm -- the Sinkhorn-Knopp algorithm -- thus making it possible to run this assignment procedure within the inner loop of standard stochastic optimization algorithms. We call the resulting label assignment process Sinkhorn Label Allocation, or SLA for short. When combined with consistency regularization, SLA yields a self-training algorithm that achieves strong performance on semi-supervised versions of CIFAR-10, CIFAR-100 and SVHN.
Citation
If you've found this repository useful in your own work, please consider citing our ICML paper:
@inproceedings{tai2021sinkhorn,
title = {{Sinkhorn Label Allocation: Semi-supervised classification via annealed self-training}},
author = {Tai, Kai Sheng and Bailis, Peter and Valiant, Gregory},
booktitle = {International Conference on Machine Learning},
year = {2021},
}
Environment
We recommend using a conda environment to manage dependencies:
$ conda env create -f environment.yml
$ conda activate sinkhorn-label-allocationUsage
SLA can be run with a basic set of options using the following command:
$ python run_sla.py --dataset cifar10 --data_path /tmp/data --output_dir /tmp/sla --run_id my_sla_run --num_labeled 40 --seed 1 --num_epochs 1024 Similarly, the FixMatch baseline can be run using run_fixmatch.py:
$ python run_fixmatch.py --dataset cifar10 --data_path /tmp/data --output_dir /tmp/sla --run_id my_fixmatch_run --num_labeled 40 --seed 1 --num_epochs 1024 The following datasets are currently supported: cifar10, cifar100, and svhn.
A complete mixed precision SLA training run with the default parameters on CIFAR-10 takes about 35 hours on a single NVIDIA Titan V.
For additional algorithm specific options, use the --help flag:
$ python run_supervised.py -- --help
$ python run_fixmatch.py -- --help
$ python run_sla.py -- --helpLicense
MIT