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If $D = \{ A, B \}$ is a dataset that contains both labelled and unlabelled data, where $A = \{ (x_i, y_i) \}_{i=1}^n$, $B = \{ x_i \}_{i=1}^m$, and $m \gg n$, then, to use self-supervised learning (for representation learning), you could follow these steps learn representations of your images $x_i$ by training a neural network $M$ with $B$ to solve a so-...


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GANs are usually trained in a self-supervised fashion, i.e. they use the unlabelled data as the supervisory signal. Note that some self-supervised learning methods are unsupervised learning techniques, given that no human-annotated data is needed. However, not all SSL techniques are used for solving an unsupervised learning task. In fact, there are SSL ...


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