Learning Representations by Maximizing Mutual Information Across Views

Introduction

AMDIM (Augmented Multiscale Deep InfoMax) is an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context.

Our paper describing AMDIM is available at: https://arxiv.org/abs/1906.00910.

Main Results

Results of AMDIM compared to other methods when evaluating accuracy using a linear classifier trained on top of representations provided by the self-supervised encoder:

Method	ImageNet	Places205
Rotation [1]	55.4	48.0
Exemplar [1]	46.0	42.7
Patch Offset [1]	51.4	45.3
Jigsaw [1]	44.6	42.2
CPC - big [1]	48.7	n/a
CPC - huge [2]	61.0	n/a
AMDIM	68.1	55.0

[1]: Results from Kolesnikov et al. [2019].
[2]: Results from Henaff et al. [2019].

Pre-trained Models

Two pre-trained models are available to download:

AMDIM-Medium

amdim_ndf256_rkhs2048_rd10.pth
This model should get 67%+ linear accuracy on the test set.

AMDIM-Large

amdim_ndf320_rkhs2560_rd10.pth
This model should get 68%+ linear accuracy on the test set.

Testing a model

To get the accuracy of a checkpointed model on ImageNet test set:

python test.py 
--dataset in128 
--input_dir <path/to/imagenet> 
<checkpoint_path.pth>

Self-Supervised Training

You should be able to get some good results on ImageNet if you have access to 4 Tesla V100 GPUs with:

CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py \
  --ndf 192 \
  --n_rkhs 1536 \
  --batch_size 480 \
  --tclip 20.0 \
  --n_depth 8 \
  --dataset IN128 \
  --amp

For GPUs older than Volta, you will need to tweak the model size to fit in the available memory. The command above will take about 15GB of memory on device 0, and slightly less on devices 1-3, when running in mixed precision (--amp). When running in FP32, memory usage will be significantly higher.

Results with the data augmentation implemented in this repo will be less than our strongest results with equivalent architecture by 1-2%. Our strongest results use augmentation based on the ImageNet policy from the Fast AutoAugment paper by Lim et al., implemented in the repo available at: https://github.com/kakaobrain/fast-autoaugment.

Using the stronger augmentation and an appropriate learning schedule, the command above should produce a bit over 63% accuracy on ImageNet using the online evaluation classifiers. With the standard torchvision augmentations the result will drop to a bit over 62%, which is still decent (significantly state-of-the-art, makes good coffee, etc.).

Evaluation Classifiers Training

Example of retraining evaluation classifiers on Places205, using an encoder checkpointed after training via self-supervised learning on ImageNet:

CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py \
  --classifiers # Add this flag to train evaluation classifiers
  --checkpoint_path ./path/to/imagenet/checkpoint.pth \
  --ndf 192 \
  --n_rkhs 1536 \
  --batch_size 480 \
  --tclip 20.0 \
  --n_depth 8 \
  --dataset Places205 \
  --input_dir /path/to/places205 \
  --amp

• When restoring from an encoder checkpoint, classifiers will be re-initialized before training again.
  
• This can be used with other datasets if you can endure the pain of figuring out how to load them (one of the greatest challenges in any computer vision project)...
  

Enabling Mixed Precision Training (--amp)

If your GPU supports half precision, you can take advantage of it when training by passing the --amp (automatic mixed precision) flag.
We use NVIDIA/apex to enable mixed precision, so you will need to have Apex installed, see: Quick Start.

Citation

@article{bachman2019amdim,
  Author={Bachman, Philip and Hjelm, R Devon and Buchwalter, William},
  Journal={arXiv preprint arXiv:1906.00910},
  Title={Learning Representations by Maximizing Mutual Information Across Views},
  Year={2019}
}

Contact

For questions please contact Philip Bachman at phil.bachman at gmail.com.