Contrastive Predictive Coding

Contrastive Predictive Coding (CPC) is a self-supervised learning technique that learns data representations by predicting future data points within a temporal context, contrasting them with other, less likely future points. Current research focuses on applying CPC to diverse domains, including financial time series analysis, WiFi sensing, and speech recognition, often integrating it with other methods like reinforcement learning or dimensionality reduction to improve performance and address challenges like non-stationarity and high dimensionality. This approach is proving valuable for tasks where labeled data is scarce or expensive, enabling the development of robust and generalizable models across various applications. The resulting representations show promise in improving the accuracy and efficiency of downstream tasks such as anomaly detection, activity recognition, and multimodal fusion.