Self-supervised Learning
I want to go through the Wikipedia series on Machine Learning and Data mining. Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
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Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals, rather than relying on externally-provided labels. In the context of neural networks, self-supervised learning aims to leverage inherent structures or relationships within the input data to create meaningful training signals. SSL tasks are designed so that solving them requires capturing essential features or relationships in the data. The input data is typically augmented or transformed in a way that creates pairs of related samples, where one sample serves as the input, and the other is used to formulate the supervisory signal. This augmentation can involve introducing noise, cropping, rotation, or other transformations. Self-supervised learning more closely imitates the way humans learn to classify objects.
During SSL, the model learns in two steps:
- The task is solved based on an auxiliary or pretext classification task using pseudo labels, which help to initialize the model parameters
- The actual task is performed using supervised or unsupervised learning.
Self supervised learning has produced promising results in tasks like audio processing.
Types
Auto associative self-supervised learning
Auto associative self-supervised learning is a specific category of self-supervised learning where a neural network is trained to reproduce or reconstruct its own input data. In other words, the model is tasked with learning a representation of the data that captures its essential features or structure, allowing it to regenerate the original input.
The training process involves presenting the model with input data and requiring it to reconstruct the same data as closely as possible. The loss function used during training typically penalized the difference between the original input and the reconstructed input. By minimizing the reconstruction error, the autoencoder learns a meaningful representation of the data in its latent space.
Contrastive Self-Supervised Learning
Contrastive self-supervised learning uses both positive and negative samples from binary classification tasks.
Examples
- Self-supervised learning is particularly suitable for speech recognition
- Google's BERT model is used to better understand the context of search queries
- OpenAI's GPT-3 is an autoregressive language model that can be used in language processing.
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