isabella232 / dapc

Deep Autoencoding Predictive Components

Overview

Deep Autoencoding Predictive Components (DAPC) is a self-supervised representation learning method for sequence data, based on the intuition that useful representations of sequence data should exhibit a simple structure in the latent space.

We encourage this latent structure by maximizing an estimate of predictive information (PI) of latent feature sequences, and regularize the learning through masked reconstruction; the full learning objective is described in [1]. Here we use the same estimate of predictive information from the recent work Dynamical Components Analysis [3] (and our implementation of PI is modified from theirs). The masked reconstruction loss was applied to pretraining encoders for speech recognition in [2].

This repository mainly demonstrates the Lorenz Attractor experiments.

Leftmost: ground-truth 3d signals. Middle left: lifted 30d signals. Middle right: noisy lifted 30d signals. Rightmost: unsupervised recovery of the 3d signals by DAPC.

Requirements

• Python 3.7+
• numpy 1.17.3
• matplotlib
• PyTorch 1.5.0

Older versions might work as well.

Usage

Download the repo

git clone https://github.com/JunwenBai/DAPC.git

To run the deterministic DAPC

./run_ddapc.sh

To run the probabilistic DAPC

./run_vdapc.sh

One can inspect the bashes to see all the options for training. By default, we use gpu:0.

Paper

If you are interested in our work, please consider cite the following paper:

@article{bai2020representation,
  title={Representation Learning for Sequence Data with Deep Autoencoding Predictive Components},
  author={Bai, Junwen and Wang, Weiran and Zhou, Yingbo and Xiong, Caiming},
  journal={arXiv preprint arXiv:2010.03135},
  year={2020}
}

References

[1] Junwen Bai, Weiran Wang, Yingbo Zhou, and Caiming Xiong. Representation Learning for Sequence Data with Deep Autoencoding Predictive Components. In International Conference on Learning Representations, 2021.

[2] Weiran Wang, Qingming Tang, and Karen Livescu. Unsupervised Pre-training of Bidirectional Speech Encoders via Masked Reconstruction. In ICASSP, 2020.

[3] Clark, D., Livezey, J. and Bouchard, K.. Unsupervised discovery of temporal structure in noisy data with dynamical components analysis. In Advances in Neural Information Processing Systems, 2019.