Joint-Embedding vs. Reconstruction: When Should You Use Each?

Joint-embedding and reconstruction are two fundamental approaches in representation learning. Joint-embedding methods, such as contrastive learning, aim to learn representations by maximizing the similarity between positive pairs and minimizing the similarity between negative pairs. Reconstruction methods, on the other hand, focus on learning representations by reconstructing the input data. The choice between these approaches depends on the specific task, dataset, and desired properties of the learned representations. For instance, joint-embedding methods are often preferred when the goal is to learn representations that are useful for downstream tasks such as classification or clustering, while reconstruction methods are typically used when the goal is to learn a generative model or to perform dimensionality reduction.