88 votes
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What is self-supervised learning in machine learning?

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 ...
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17 votes

What is self-supervised learning in machine learning?

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 ...
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10 votes

What is the relation between semi-supervised and self-supervised visual representation learning?

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 ...
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8 votes

What is the relation between semi-supervised and self-supervised visual representation learning?

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 ...
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5 votes

How to generate labels for self-supervised training?

How can I generate the target label from the other data in the dataset? If you are asking how you can create the learning signal in SSL, when given an unlabelled dataset, for learning representations ...
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5 votes

What is self-supervised learning in machine learning?

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 ...
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5 votes

What is the relation between semi-supervised and self-supervised visual representation learning?

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 ...
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4 votes

How are generative adversarial networks trained?

Compare generated and real data All the results produced by G are always considered "wrong" by definition, even for a very good generator. You provide the discriminative neural network $D$ with a ...
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3 votes
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Is it realistic to train a transformer-based model (e.g. GPT) in a self-supervised way directly on the Mel spectrogram?

The reason most music-generation models use discrete representations is because the long-term structures of music are very challenging to model. Note that the MIDI data in MAESTRO (used in the two ...
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3 votes

How to understand the concept of self-supervised learning in AI?

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 ...
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3 votes
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What is the purpose of the GAN?

GANs were invented in a bar somewhere in Montreal, Canada. At said bar, the idea was that neural networks could be used for generating new examples from an existing distribution. This was the problem: ...
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2 votes

Is it possible to pre-train a CNN in a self-supervised way so that it can later be used to solve an instance segmentation task?

Is it possible to use SSL to pre-train e.g. a faster R-CNN on a pretext task (for example, rotation), then use this pre-trained model for instance segmentation with the aim to get better accuracy? ...
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2 votes

How are generative adversarial networks trained?

A discriminative network ($D$) learns to discriminate by definition - we provide it with the true and the generated data, and let it learn by itself how to discriminate between the two. Therefore, ...
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2 votes

How to understand the concept of self-supervised learning in AI?

Andrew Zisserman, who is a pioneer in the field of self-supervised learning, described self-supervised learning in a talk at ICML as: Self-supervised Learning is a form of unsupervised learning ...
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1 vote

1D Sequence Classification with self-supervised learning

In SSL (language modelling, for example), you do not have any explicit labels, just sequences of words that make sense together. SSL tries to model the language by next-word prediction, but the words ...
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1 vote

Is it realistic to train a transformer-based model (e.g. GPT) in a self-supervised way directly on the Mel spectrogram?

These papers are also very close to what I meant in the question (too long for a comment). The following references come mostly from work on speech recognition. Mockingjay In this work, they use an ...
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1 vote

What are some most promising ways to approximate common sense and background knowledge?

Note that, in knowledge representation and reasoning, common-sense knowledge is traditionally represented as sentences (in logic). For example, one possible sentence that you could store in a ...
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1 vote

Is it possible to use self-supervised learning on different images for the pretext and downstream tasks?

No, the images do not need to be same. You can use different images for downstream task however you need to do some changes in model definition while loading model state_dict as CNN architecture used ...
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1 vote

Is there a way to get landmark features automatically learned by a neural network?

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. ...
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1 vote

What is the difference between distant supervision and self-supervision?

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 ...
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