Skip to content
{{ message }}

# lorenmt / maxl Public

The implementation of "Self-Supervised Generalisation with Meta Auxiliary Learning" [NeurIPS 2019].

Switch branches/tags
Could not load branches
Nothing to show

## Files

Failed to load latest commit information.
Type
Name
Commit time

# MAXL - Meta Auxiliary Learning

This repository contains the source code to support the paper: Self-Supervised Generalisation with Meta Auxiliary Learning, introduced by Shikun Liu, Andrew J. Davison and Edward Johns.

## Update

Nov 2021: We have implemented the first order approximation of MAXL framework, which would speed up 4 - 6 times training time compared to the original implementation. The first order approximation is based on the finite difference method, inspired by DARTS. No more tedious forward functions for the inner loop optimisation now. Enjoy. :)

## Requirements

MAXL was written in python 3.7 and pytorch 1.0. We recommend running the code through the same version while we believe the code should also work (or can be easily revised) within other versions.

## Models & Datasets

This repository includes three models model_vgg_single.py, model_vgg_human.py and model_vgg_maxl.py representing baselines Single, Human and our proposed algorithm MAXL with backbone architecture VGG-16. These three models are trained with 4-level CIFAR-100 dataset which should easily reproduce part of the results in Figure 3.

In create_dataset.py, we define an extended version of CIFAR-100 with 4-level hierarchy built on the original CIFAR100 class in torchvision.datasets (see the full table for semantic classes in Appendix A). To fetch one batch of input data with kth hierarchical labels as defined below, we have train_data which represents the input images and train_label which represents the 4-level hierarchical labels: train_label[:, k], k = 0, 1, 2, 3 fetches 3, 10, 20 and 100-classes respectively.

train_data, train_label[:, k] = cifar100_train_dataset.next()


## Training MAXL

The source code provided gives an example of training primary task of 20 classes train_label[:, 2] and auxiliary task of 100 classes train_label[:, 3] with hierarchical structure \psi[i]=5. To run the code, please create a folder dataset to download CIFAR-100 dataset in this directory or you may redefine the dataset root path as your wish. It is straightforward to revise the code evaluating other hierarchies and play with other datasets found in torchvision.datasets.

Note that: make sure len(psi) be the number of primary classes, and sum(psi) be the number of total auxiliary classes, e.g. psi = [2,3,4] representing total 3 primary classes and total 9 auxiliary classes by splitting each corresponding primary class into 2, 3, and 4 different auxiliary classes.

Training MAXL from scratch typically requires 30 hours in GTX 1080, and training the baselines methods Single and Human requires 2-4 hours from scratch.

## Citation

If you found this code/work to be useful in your own research, please considering citing the following:

@inproceedings{liu2019maxl,
title={Self-supervised generalisation with meta auxiliary learning},
author={Liu, Shikun and Davison, Andrew and Johns, Edward},
booktitle={Advances in Neural Information Processing Systems},
pages={1677--1687},
year={2019}
}


## Contact

If you have any questions, please contact sk.lorenmt@gmail.com.

## About

The implementation of "Self-Supervised Generalisation with Meta Auxiliary Learning" [NeurIPS 2019].

## Releases

No releases published

## Packages 0

No packages published