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Introduction The term self-supervised learning (SSL) has been used (sometimes differently) in different contexts and fields, such as representation learning [1], neural networks, robotics [2], natural language processing, and reinforcement learning. In all cases, the basic idea is to automatically generate some kind of supervisory signal to solve some task (...


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Self-supervised learning is when you use some parts of the samples as labels for a task that requires a good degree of comprehension to be solved. I'll emphasize these two key points, before giving an example: Labels are extracted from the sample, so they can be generated automatically, with some very simple algorithm (maybe just random selection). The task ...


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Semi-supervised learning Semi-supervised learning is the collection of machine learning techniques where there are two datasets: a labelled one and an unlabelled one. There are two main problems that can be solved using semi-supervised learning: transductive learning (i.e. label the given unlabelled data) and inductive learning (generalization) (i.e. find a ...


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Both semi-supervised and self-supervised methods are similar in the sense that the goal is to learn with fewer labels per class. The way both formulate this is quite different: Self-Supervised Learning: This line of work aims to learn image representations without requiring human-annotated labels and then use those learned representations on some ...


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The previous answer has given a good insight into the difference between two areas. I would like to give more examples. Semi-Supervised Learning work with improving the data set by adding up new examples. There are iterative systems where we train a model on a given dataset and improve the model further after deploying it on the real world by adding ...


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Self-supervised visual recognition is often applied to representation learning. Here we first learn features on unlabeled data (representation learning), and then learn the real model on features extracted from the labeled data. This especially makes sense when we have a lot of unlabeled data and few labeled data. The features can be learned by solving so ...


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I don't think your interpretation is correct. Take images as example. Supervised Learning e.g. classification (maybe use CNN with a L2 loss function) Assume you have many images with different labels. You wish to find a function to approximate the function $y=f(x)$ given a lot of $(\hat x, \hat y)$ sample pairs. Unsupervised Learning e.g. clustering (...


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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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No, you can't. In CNN, if you want to detect landmark, you need to prepare data with region box, it's coordinates, width, height, than number of points that should be detected and points coordinates. Then your target vector should be, This is your target vector. Optionally you can use YOLO algorithm.


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The main difference between distant supervision (as described in the link you provided) and self-supervision lies on the task the network is trained on. Distant supervision focuses on generating weak labels for the very same task that would be tackled with supervised labels, and the final result could be directly used for that matter. Self-supervision is a ...


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