Representation Learning with Contrastive Predictive Coding (1807.03748v2)

Abstract: While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments.

• The paper proposes Contrastive Predictive Coding, a novel unsupervised method that leverages autoregressive models to predict future latent representations from high-dimensional data.
• It compresses data into a latent space and uses a contrastive loss with negative sampling to maximize mutual information between past and future observations.
• Experiments across audio, vision, NLP, and reinforcement learning domains demonstrate CPC's effectiveness and versatility in representation learning.

Representation Learning with Contrastive Predictive Coding

The paper "Representation Learning with Contrastive Predictive Coding" by Aaron van den Oord, Yazhe Li, and Oriol Vinyals introduces a novel approach to unsupervised learning that aims to extract useful representations from high-dimensional data. The proposed method, termed Contrastive Predictive Coding (CPC), leverages the principles of predictive coding and contrastive learning to learn high-level abstract representations across diverse data modalities including speech, images, text, and reinforcement learning environments.

Key Insights and Methodology

The core idea of CPC is predicated on predicting future observations in a latent space using autoregressive models. This approach capitalizes on the rich contextual dependencies present within the data. The unsupervised learning framework proceeds in three main steps:

1. Compression into Latent Space: High-dimensional data is mapped into a lower-dimensional latent space using a non-linear encoder. This step ensures that the subsequent predictions are computationally tractable.
2. Autoregressive Modeling: Using an autoregressive model within this latent space, CPC predicts future latent representations based on past observations, capturing the temporal dependencies in the data.
3. Contrastive Loss Function: CPC's loss function employs Noise-Contrastive Estimation (NCE) to differentiate between true future observations and a set of negative samples. This probabilistic contrastive loss maximizes the mutual information between the encoded representations of the present and future observations, which is crucial for learning useful features.

Experimental Evaluation

The paper demonstrates the efficacy of CPC across four domains:

1. Audio

Using the LibriSpeech dataset, CPC significantly outperforms traditional methods such as MFCC and even approaches the performance of fully supervised models. For example, in phone classification, CPC achieves an accuracy of 64.6% compared to 39.7% for MFCC and 74.6% for a supervised model. The versatility of CPC is further highlighted through its ability to capture both phonetic and speaker identity information.

2. Vision

In the vision domain, CPC applies its framework to the ILSVRC ImageNet dataset. After unsupervised training, a linear classifier achieves a top-1 accuracy of 48.7% and a top-5 accuracy of 73.6%, substantially surpassing previous state-of-the-art results in unsupervised image classification tasks.

3. Natural Language

CPC's application to NLP utilizes the BookCorpus dataset to learn sentence embeddings. On standard NLP benchmarks, CPC offers competitive performance, achieving accuracies of 76.9% on MR, 80.1% on CR, 91.2% on Subj, 87.7% on MPQA, and 96.8% on TREC. These results are comparable to those achieved by other prominent unsupervised methods like skip-thought vectors.

4. Reinforcement Learning

In reinforcement learning, CPC is integrated as an auxiliary loss within the batched A2C framework. The evaluations on various DeepMind Lab environments demonstrate notable improvements in four out of five tasks, showcasing CPC's potential to enhance learning efficiency and policy performance.

Implications and Future Directions

The proposed CPC framework is notable for its simplicity and computational efficiency. It achieves robust performance across a wide range of domains, underscoring its general applicability. The theoretical underpinnings of maximizing mutual information between latent representations while utilizing contrastive learning provide a solid foundation for future work.

Practical Implications: CPC can be readily incorporated into existing architectures, providing a powerful tool for unsupervised learning that doesn't sacrifice performance. This has immediate applications in domains where labeling data is costly or infeasible, such as speech recognition, image analysis, and reinforcement learning.

Theoretical Implications: By focusing on mutual information, CPC bridges the gap between contrastive learning techniques and autoregressive models, offering a new lens through which to view representational learning. This could inspire new research into hybrid models that further exploit these connections.

Future Developments: Future research could explore enhancements to the autoregressive model, such as integrating recent advances in masked convolutional architectures or self-attention networks. Additionally, the application of CPC to other complex domains, such as multi-modal data or more intricate reinforcement learning tasks, is a promising avenue for further investigation.

In conclusion, Contrastive Predictive Coding represents a significant step forward in unsupervised learning, providing a versatile and effective approach to extracting powerful representations from high-dimensional data across various domains. Its potential for practical applications and theoretical contributions makes it a valuable addition to the field of artificial intelligence.