How is contrastive predictive coding (CPC) used in SSL?

Contrastive Predictive Coding (CPC) is a self-supervised learning (SSL) technique that trains models to extract meaningful representations by predicting future data points from past context. The core idea is to learn latent representations that capture the underlying structure of sequential data, such as audio, text, or images, by contrasting correct future predictions with incorrect ones. Instead of relying on labeled data, CPC uses a contrastive loss to encourage the model to distinguish between real future observations and artificially generated negative samples. This approach enables the model to learn useful features without manual annotations, making it effective for tasks like speech recognition, natural language processing, and image analysis.

CPC operates in three main steps. First, an encoder processes input data (e.g., audio frames or image patches) into compressed latent representations. Second, an autoregressive model (like a GRU or Transformer) aggregates these latent vectors into a context vector that summarizes the history of the sequence. Third, the model uses this context to predict future latent representations multiple steps ahead. For example, in audio processing, the encoder might convert raw waveform segments into embeddings, and the autoregressive model could predict embeddings for the next 0.5 seconds of audio. To train the model, a contrastive loss is applied: for each correct future prediction, the model is tasked with identifying it among a set of randomly sampled “negative” examples from other sequences. This forces the model to learn representations that are discriminative and temporally coherent.

A practical example of CPC in SSL is its application to speech recognition. Here, raw audio is split into overlapping frames, and the model learns to predict the latent embeddings of future frames from past context. By contrasting true future frames with unrelated audio snippets, the model learns to capture phonetic or semantic features useful for downstream tasks like speaker identification. Similarly, in computer vision, CPC can be adapted by treating images as sequences of patches. The model predicts latent representations of patches further down the sequence, encouraging it to learn spatial relationships or object structures. CPC’s efficiency comes from its focus on local structure and its ability to scale to large datasets, as negative sampling avoids the computational cost of comparing all possible pairs. This makes it a versatile tool for SSL across domains.