Compressive Autoencoder

Getting started

The quickest way to start experimenting is to use this model trained on this smaller dataset. An arbitrary dataset can be constructed by downloading frames using the scripts provided here.

See wiki for more details, download links and further results.

Training

python train.py --config ../configs/train.yaml

Example train.yaml:

exp_name: training

num_epochs: 1
batch_size: 16
learning_rate: 0.0001

# start fresh
resume: false
checkpoint: null
start_epoch: 1

batch_every: 1
save_every: 10
epoch_every: 1
shuffle: true
dataset_path: datasets/yt_small_720p
num_workers: 2
device: cuda

Testing

Given a trained model (checkpoint), perform inference on images @ dataset_path.

python test.py --config ../configs/test.yaml

Example test.yaml:

exp_name: testing
checkpoint: model.state
batch_every: 100
shuffle: true
dataset_path: datasets/testing
num_workers: 1
device: cuda

Note: Currently, smoothing (i.e. linear interpolation in smoothing.py) is used in order to account for the between-patches noisy areas due to padding (this still needs further investigation).

Results

• cae_32x32x32_zero_pad_bin model
• roughly 5.8 millions of optimization steps
• randomly selected and downloaded 121,827 frames
• left: original, right: reconstructed

References

• [1] Lossy Image Compression with Compressive Autoencoders, Theis et al.
• [2] Variable Rate Image Compression with Recurrent Neural Networks, Toderici et al.