Self-Supervised Learning Methods

• Self-supervised learning methods are techniques that generate proxy tasks from unlabeled data to learn robust and transferable representations.
• They include generative, contrastive, predictive, and clustering-based approaches, each with unique objective functions and network designs.
• These methods have achieved competitive performance across vision, language, audio, and graph domains, challenging conventional supervised strategies.

Self-supervised learning methods are a class of representation learning techniques that leverage unlabeled data by constructing proxy tasks—pretext or auxiliary objectives—whose labels or training signals are derived automatically from the data itself. This paradigm enables learning rich, transferable features without human annotation, and has achieved performance competitive with, or even surpassing, supervised pre-training across vision, language, audio, graph, and other modalities. Methods are typically categorized into generative, contrastive, predictive, and clustering-based approaches, each with characteristic objective functions, network designs, and theoretical underpinnings. Below are the main families, foundational principles, state-of-the-art advances, practical workflows, cross-domain performance, and open challenges in self-supervised learning methods (Ericsson et al., 2021).

1. Formal Foundations and Principal Objectives

Let $\mathcal{D} = \{x_i\}$ denote a set of unlabeled samples drawn from $p_\mathrm{data}$ over input space $\mathcal{X}$. The goal is to learn an encoder $f_\theta : \mathcal{X} \rightarrow \mathbb{R}^d$—and sometimes a decoder $g_\phi$—by minimizing a self-supervised loss: $$L(\theta, \phi) = \mathbb{E}_{x \sim \mathcal{D}}\, \ell(x; f_\theta, [g_\phi])$$ The choice of loss $\ell$ determines the family:

• Generative: $g_\phi$ reconstructs $x$ (or part of it) from $f_\theta(x)$, e.g., in autoencoders or inpainting models.
• Contrastive: maximizes similarity of “positive” (augmented/related) pairs $(x, x^+)$, while pushing apart “negative” pairs $(x, x^-)$, as in InfoNCE.
• Clustering-based: assigns each $z_i=f_\theta(x_i)$ to one of $K$ clusters, using assignments as pseudo-labels and updating with cross-entropy.
• Predictive: predicts information about transformations or context, e.g., rotation, patch order, future content—framing a classification or regression task.

These objectives, often with additional regularization, are designed to maximize agreement on shared (task-relevant) content and suppress task-irrelevant information (Tsai et al., 2020).

2. Representative Algorithmic Families

2.1 Generative Methods

• Autoencoders (AE/Denoising/Variational/Masked):

$$L_\mathrm{rec} = \mathbb{E}_x\, \|x - g_\phi(f_\theta(x))\|_2^2$$

Denoising AE adds corruption, VAEs regularize with a KL divergence, and masked autoencoders reconstruct only masked-out input patches.

• Inpainting/Context Encoders: Mask regions of $x$, reconstruct missing patch, [ L_\mathrm{inpaint} = \mathbb{E}\, |x_\mathrm{patch} - g_\phi(f_\theta(x_\mathrm{masked}))\