Unsupervised Learning of Visual Structure using Predictive Generative Networks (1511.06380v2)

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.

• The paper proposes a novel predictive generative network framework that leverages unsupervised learning to accurately forecast future visual frames.
• It utilizes a CNN-LSTM-deCNN architecture trained with a weighted combination of mean-squared error and adversarial loss, significantly reducing prediction errors on synthetic datasets.
• The findings demonstrate improved latent structure decoding, highlighting potential applications in object recognition and generalizable visual representation learning.

Unsupervised Learning of Visual Structure using Predictive Generative Networks

The paper "Unsupervised Learning of Visual Structure using Predictive Generative Networks" investigates the potential of predictive generative networks (PGN) for unsupervised learning in visual sequence prediction tasks. The authors explore the utilization of deep neural networks for creating internal models that can anticipate future states of synthetic video sequences. This research is anchored in the notion that prediction serves as a potent unsupervised loss, enabling the development of representations with transformation tolerance, which in turn can generalize well to distinct tasks, such as static image classification.

The central framework employed is a CNN-LSTM-deCNN architecture, which integrates feature representation learning with the learning of temporal dynamics through Encoder-Recurrent-Decoder (ERD) architectures. This design combines convolutional neural networks (CNNs) for feature extraction, Long Short-Term Memory (LSTM) networks for capturing temporal dependencies, and deconvolutional networks (deCNN) for image generation. The networks are trained using a combination of weighted mean-squared error (MSE) and adversarial loss (AL), the latter being derived from the Generative Adversarial Network (GAN) framework.

Key Findings

One of the primary findings is the model’s ability to achieve state-of-the-art performance in synthetic video prediction, notably in the "bouncing balls" and rotating faces datasets. The paper reports an average squared one-step-ahead prediction error that is significantly lower than previous models, such as restricted Boltzmann machines (RBMs) and Deep Temporal Sigmoid Belief Networks (DTSBN).

The PGNs are shown to successfully extrapolate and generate realistic predictions for computer-generated faces undergoing rotation. A key aspect of this is the representation of latent structures of three-dimensional objects, where the predictive models demonstrated superior decoding of latent variables compared to autoencoders trained only with reconstruction loss. This elucidates the model's capability in capturing the essential components of the visual generative process.

Implications

The implications of this research are manifold, particularly for the fields of computer vision and unsupervised learning. Prediction as an unsupervised loss allows neural networks to learn rich object representations that can be adapted to other types of tasks, even when those tasks require generalization beyond the specific predictive domain. This methodology shows promise in developing models that are aligned with the human cognitive process where the anticipation of future events plays a crucial role in perception and understanding.

From a practical standpoint, the predictive models developed can contribute to enhancing object recognition systems, especially in contexts where models must learn effectively from limited exposure to novel objects. Moreover, the findings underscore the importance of non-traditional loss functions such as adversarial loss in producing high-fidelity image reconstructions that are essential for synthetic and real-world imagery.

Future Directions

Given the simplified artificial settings of the current paper, extending these experiments with natural, complex imagery remains a critical future endeavor. Investigating how predictive generative models can scale to incorporate the myriad transformation degrees found in real-world scenarios is an exciting trajectory for further research. Consideration of adaptive predictive frameworks capable of dynamic adjustment to varying environmental conditions could lead to more robust and comprehensive AI systems.

In summary, this research outlines a compelling case for utilizing prediction as a cornerstone for unsupervised learning in visual sequence tasks, offering important insights for both theoretical exploration and practical applications.