Unsupervised Learning of Visual Structure using Predictive Generative Networks

TL;DR

This paper demonstrates that deep neural networks trained to predict future video frames can learn internal representations of 3D objects, generalize to new tasks, and outperform models trained only with reconstruction loss.

Contribution

It introduces a predictive generative network framework that learns rich, transformation-tolerant representations through unsupervised future frame prediction.

Findings

Achieves state-of-the-art performance in video prediction tasks.

Learns representations that generalize to object classification.

Outperforms reconstruction-only models in generalization.

Abstract

The ability to predict future states of the environment is a central pillar of intelligence. At its core, effective prediction requires an internal model of the world and an understanding of the rules by which the world changes. Here, we explore the internal models developed by deep neural networks trained using a loss based on predicting future frames in synthetic video sequences, using a CNN-LSTM-deCNN framework. We first show that this architecture can achieve excellent performance in visual sequence prediction tasks, including state-of-the-art performance in a standard 'bouncing balls' dataset (Sutskever et al., 2009). Using a weighted mean-squared error and adversarial loss (Goodfellow et al., 2014), the same architecture successfully extrapolates out-of-the-plane rotations of computer-generated faces. Furthermore, despite being trained end-to-end to predict only pixel-level information, our Predictive Generative Networks learn a representation of the latent structure of the underlying three-dimensional objects themselves. Importantly, we find that this representation is naturally tolerant to object transformations, and generalizes well to new tasks, such as classification of static images. Similar models trained solely with a reconstruction loss fail to generalize as effectively. We argue that prediction can serve as a powerful unsupervised loss for learning rich internal representations of high-level object features.