Before discussing big models, let's first think about a question: How does AI "learn" to recognize cats, translate sentences, and write code? The traditional answer is' feed it a large amount of labeled data '. But manual annotation is expensive and slow, and the original data (pictures, text, audio) on the Internet is almost unlimited. Self Supervised Learning (SSL) is a type of method developed to address this contradiction: it does not rely on artificial labels, but instead "creates" supervised signals from the data itself, allowing the model to teach itself.
Core idea: Hide a part of the data and let the model guess
The key to self supervised learning is to design a 'pretext task'. Its approach is usually to obscure a portion of the data, allowing the model to predict the obscured content based on the remaining portion:
Text: Cover certain words in the sentence and let the model guess what these words are (masked language model);
Image: Rotate, crop, and color change the images to let the model determine if they come from the same image (contrastive learning);
Voice and video: predict the following segments based on the previous segments (autoregressive prediction).
The "answers" to these tasks are hidden in the raw data and do not require human annotation, so massive unlabeled data can be utilized on a large scale.
Three mainstream technological routes
1. Comparative LearningMake different "perspectives" (different cropping, enhancement) of the same image closer in the feature space, and make features of different images farther apart. SimCLR has demonstrated that even without any manual labeling, the learned visual features can approach the effectiveness of supervised learning on downstream tasks.
2. Masked Language ModelingBERT randomly masks about 15% of the words in the input text and trains the model to restore them. In this way, the model must understand the context and syntax semantics in order to fill in the blanks correctly.
3. Autoregressive PredictionThe path chosen by the GPT series is to 'predict the next word'. Given the previous context, the model continuously predicts the next token, and the training signal is the text itself, which naturally does not require annotation.
Pre training+fine-tuning: two-stage paradigm
Self supervised learning usually does not directly produce usable application models, but first pre trains on massive unlabeled data to obtain a "universal base", and then fine tunes on specific tasks with a small amount of labeled data, or only trains a simple classification head (linear probing). This is exactly the standard process for today's big language models and visual foundation models.
Why is it so important
Self supervised learning has changed the "data bottleneck" from "labeling cost" to "computing power and data scale", enabling models to learn general representation on Internet level data. It can be said that the reason why models such as BERT and GPT can read thousands of books is due to the credit of self supervised pre training.
Limitations and Challenges
Pre training tasks may not be consistent with downstream tasks, and the learned representations may not be transferred satisfactorily at times;
The training cost is high and requires a large amount of computing power;
The data itself may be biased, and the model will accept everything according to the order.
Summary
Self supervised learning answers a simple yet profound question: AI can learn without labels. It enables the model to generate supervised signals from the data itself, becoming the cornerstone of modern deep learning pre training paradigms. Understanding self supervision means understanding why today's big models are able to 'get bigger and stronger'.
[Reference source]
Chen et al., "A Simple Framework for Contrastive Learning of Visual Representations"（SimCLR），arXiv:2002.05709，2020
Devlin et al., "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding"，arXiv:1810.04805，2019
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