An in-depth exploration of SSL's self-supervised learning methods

Self-supervised learning (SSL) is a method of unsupervised learning that uses unlabeled data to train a model. The core idea is to let the model learn a representation of the data without human labels. Once a model learns how to represent data, it can be applied to downstream tasks with less labeled data and achieve better performance than models without self-supervised learning. Through self-supervised learning, the model can use the implicit information in the data to learn, for example, by predicting the rotation of the data, color changes, etc. This method can provide an effective learning method in the absence of labeled data, and is of great significance for solving the problem of large-scale data training.

Self-supervised learning (SSL) steps

1. Programmatically generate input data and labels from unlabeled data based on understanding of the data

2. Pre- Training: Train the model using the data/labels from the previous step

3. Fine-tuning: Use the pre-trained model as initial weights to train the task of interest

The importance of self-supervised learning (SSL) Self-supervised learning has achieved remarkable success in various fields, such as text, image/video, speech and graphics. It can help us understand the structure and attribute information in graph data and mine useful information from unlabeled data. Therefore, self-supervised learning is good at mining unlabeled data.

Categories of Self-Supervised Learning (SSL)

1. Generation method: Restore original

information Non-autoregressive: Mask markers/pixels and predict masked markers/pixels (e.g. , Masked Language Modeling (MLM))

b. Autoregressive: Predict the next tag/pixel Relative position, predict whether the next segment is the next sentence)

b: Predict the id of each sample in the cluster

c: Predict the image rotation angle

3. Comparison Learning (aka Contrastive Instance Discrimination): Establishing a binary classification problem based on positive and negative sample pairs created by augmentation

4. Bootstrap method: Use two similar but different networks to learn from augmented pairs of the same sample Same representation

5. Regularization: Add loss and regularization terms based on assumptions/intuitions:

a: Positive pairs should be similar

b: Different in the same batch The output of the sample should be different

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