Self-supervised Learning

Self-Supervised Learning makes use of unlabeled data train the machine learning model and does not require manual annotations. Self-Supervised Learning uses the data itself to create the training labels, hence , removing the need for humans in the labeling process.

ℹ️ Self-supervised learning methods are well-suited to environments where data is plentiful, but labels are not. It has revolutionized several fields such as speech processing, computer vision, and natural language processing (NLP).

Types of Self-Supervised Learning:

  • Image Inpainting In this type of learning, a portion of an image is removed and the model is trained to fill in the missing pixels. It is highly relevant for video analysis, recognizing actions or objects, and improving image quality.
  • Instance Discrimination Instance recognition involves providing a model with two examples and training the model to determine whether they are from the same group or not. As long as one can identify patterns from exemplars, one can train the model to retrieve relevant representations.
  • Rotation Prediction Rotation prediction works on rotating an image to a random degree and training a model to predict the angle of rotation. This results in models that can perceive the orientation of an object and thus have better visual recognition with regards to object manipulation tasks.
  • Contrastive Learning Contrastive learning involves contrasting the embeddings of the different parts of the input to identify dependencies. One example could be training the model to predict the immediate future based on the current frame in a video.
  • Future Prediction Future prediction involves creating a model that predicts future frames in a video from the present frame based on temporal relations between the frames in the video. Using this type of learning, it's evident to make accurate predictions around time-dependent data such as climate change or financial risk modeling.