What is the relation between semi-supervised and self-supervised visual representation learning?What is self-supervised learning in machine learning?What is the difference between artificial intelligence and machine learning?Are Machine-Learning and Self-Learning really possible?What is the difference between assisted and unassisted learning in relation to AI?What is the difference between encoders and auto-encoders?What are the differences between uniform-cost search and greedy best-first search?What is self-supervised learning in machine learning?What are the differences between learning by analogy, inductive learning and explanation based learning?What is the difference between Problem Modelling and Problem Representation?What is the difference between reinforcement learning and optimal control?What are the key differences between cellular neural network and convolutional neural network?
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What is the relation between semi-supervised and self-supervised visual representation learning?
What is self-supervised learning in machine learning?What is the difference between artificial intelligence and machine learning?Are Machine-Learning and Self-Learning really possible?What is the difference between assisted and unassisted learning in relation to AI?What is the difference between encoders and auto-encoders?What are the differences between uniform-cost search and greedy best-first search?What is self-supervised learning in machine learning?What are the differences between learning by analogy, inductive learning and explanation based learning?What is the difference between Problem Modelling and Problem Representation?What is the difference between reinforcement learning and optimal control?What are the key differences between cellular neural network and convolutional neural network?
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What's the differences between semi-supervised learning and self-supervised visual representation learning, and how they are connected?
machine-learning difference supervised-learning self-supervised-learning semi-supervised-learning
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What's the differences between semi-supervised learning and self-supervised visual representation learning, and how they are connected?
machine-learning difference supervised-learning self-supervised-learning semi-supervised-learning
asked May 12 at 0:23
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What's the differences between semi-supervised learning and self-supervised visual representation learning, and how they are connected?
machine-learning difference supervised-learning self-supervised-learning semi-supervised-learning
asked May 12 at 0:23
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What's the differences between semi-supervised learning and self-supervised visual representation learning, and how they are connected?
machine-learning difference supervised-learning self-supervised-learning semi-supervised-learning
machine-learning difference supervised-learning self-supervised-learning semi-supervised-learning
asked May 12 at 0:23
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asked May 12 at 0:23
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Semi-supervised learning is a combination of supervised and unsupervised learning. In semi-supervised learning, 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 and inductive learning (generalisation). In the case of transductive learning, the goal is to label the given unlabelled data. In the case of inductive learning, the goal is to find a function that maps inputs to outputs (like classification).
Self-supervised learning (SSL) is a special case of supervised learning where the training dataset is not manually labelled by a human, but the labels (for each training sample) are generated by exploiting correlations of the inputs or between different inputs (coming from different sensor modalities). For example, in the context of robotics, a robot can be equipped with different sensors. The output of these sensors can be correlated. For example, suppose a robot is equipped with a camera and a proximity sensor (that senses the presence of obstacles around the robot). The output of these sensors is clearly correlated (e.g. a proximity sensor detects an object when there is an object in the camera frame). There are thus several ways of performing self-supervised learning. It depends on the problem, the available sensors and data.
Self-supervised learning can also refer to an unsupervised learning that is used to find features of the unlabelled data (this task is called "representation learning"), which are used to "label" the same unlabelled data.
RL could actually be considered an instance of self-supervised learning, because the inputs (the experience) is used to label the quality of the states (e.g. by estimating the value function) or actions.
SSL has thus slightly different definitions depending on the context.
How are semi and self-supervised learning related? For example, self-supervised learning can be used to construct the labelled dataset that is used in semi-supervised learning.
answered May 12 at 1:16
nbronbro
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Semi-supervised learning is a combination of supervised and unsupervised learning. In semi-supervised learning, 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 and inductive learning (generalisation). In the case of transductive learning, the goal is to label the given unlabelled data. In the case of inductive learning, the goal is to find a function that maps inputs to outputs (like classification).
Self-supervised learning (SSL) is a special case of supervised learning where the training dataset is not manually labelled by a human, but the labels (for each training sample) are generated by exploiting correlations of the inputs or between different inputs (coming from different sensor modalities). For example, in the context of robotics, a robot can be equipped with different sensors. The output of these sensors can be correlated. For example, suppose a robot is equipped with a camera and a proximity sensor (that senses the presence of obstacles around the robot). The output of these sensors is clearly correlated (e.g. a proximity sensor detects an object when there is an object in the camera frame). There are thus several ways of performing self-supervised learning. It depends on the problem, the available sensors and data.
Self-supervised learning can also refer to an unsupervised learning that is used to find features of the unlabelled data (this task is called "representation learning"), which are used to "label" the same unlabelled data.
RL could actually be considered an instance of self-supervised learning, because the inputs (the experience) is used to label the quality of the states (e.g. by estimating the value function) or actions.
SSL has thus slightly different definitions depending on the context.
How are semi and self-supervised learning related? For example, self-supervised learning can be used to construct the labelled dataset that is used in semi-supervised learning.
answered May 12 at 1:16
nbronbro
3,3442826
$endgroup$
add a comment |
$begingroup$
Semi-supervised learning is a combination of supervised and unsupervised learning. In semi-supervised learning, 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 and inductive learning (generalisation). In the case of transductive learning, the goal is to label the given unlabelled data. In the case of inductive learning, the goal is to find a function that maps inputs to outputs (like classification).
Self-supervised learning (SSL) is a special case of supervised learning where the training dataset is not manually labelled by a human, but the labels (for each training sample) are generated by exploiting correlations of the inputs or between different inputs (coming from different sensor modalities). For example, in the context of robotics, a robot can be equipped with different sensors. The output of these sensors can be correlated. For example, suppose a robot is equipped with a camera and a proximity sensor (that senses the presence of obstacles around the robot). The output of these sensors is clearly correlated (e.g. a proximity sensor detects an object when there is an object in the camera frame). There are thus several ways of performing self-supervised learning. It depends on the problem, the available sensors and data.
Self-supervised learning can also refer to an unsupervised learning that is used to find features of the unlabelled data (this task is called "representation learning"), which are used to "label" the same unlabelled data.
RL could actually be considered an instance of self-supervised learning, because the inputs (the experience) is used to label the quality of the states (e.g. by estimating the value function) or actions.
SSL has thus slightly different definitions depending on the context.
How are semi and self-supervised learning related? For example, self-supervised learning can be used to construct the labelled dataset that is used in semi-supervised learning.
answered May 12 at 1:16
nbronbro
3,3442826
$endgroup$
add a comment |
$begingroup$
Semi-supervised learning is a combination of supervised and unsupervised learning. In semi-supervised learning, 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 and inductive learning (generalisation). In the case of transductive learning, the goal is to label the given unlabelled data. In the case of inductive learning, the goal is to find a function that maps inputs to outputs (like classification).
Self-supervised learning (SSL) is a special case of supervised learning where the training dataset is not manually labelled by a human, but the labels (for each training sample) are generated by exploiting correlations of the inputs or between different inputs (coming from different sensor modalities). For example, in the context of robotics, a robot can be equipped with different sensors. The output of these sensors can be correlated. For example, suppose a robot is equipped with a camera and a proximity sensor (that senses the presence of obstacles around the robot). The output of these sensors is clearly correlated (e.g. a proximity sensor detects an object when there is an object in the camera frame). There are thus several ways of performing self-supervised learning. It depends on the problem, the available sensors and data.
Self-supervised learning can also refer to an unsupervised learning that is used to find features of the unlabelled data (this task is called "representation learning"), which are used to "label" the same unlabelled data.
RL could actually be considered an instance of self-supervised learning, because the inputs (the experience) is used to label the quality of the states (e.g. by estimating the value function) or actions.
SSL has thus slightly different definitions depending on the context.
How are semi and self-supervised learning related? For example, self-supervised learning can be used to construct the labelled dataset that is used in semi-supervised learning.
answered May 12 at 1:16
nbronbro
3,3442826
$endgroup$
Semi-supervised learning is a combination of supervised and unsupervised learning. In semi-supervised learning, 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 and inductive learning (generalisation). In the case of transductive learning, the goal is to label the given unlabelled data. In the case of inductive learning, the goal is to find a function that maps inputs to outputs (like classification).
Self-supervised learning (SSL) is a special case of supervised learning where the training dataset is not manually labelled by a human, but the labels (for each training sample) are generated by exploiting correlations of the inputs or between different inputs (coming from different sensor modalities). For example, in the context of robotics, a robot can be equipped with different sensors. The output of these sensors can be correlated. For example, suppose a robot is equipped with a camera and a proximity sensor (that senses the presence of obstacles around the robot). The output of these sensors is clearly correlated (e.g. a proximity sensor detects an object when there is an object in the camera frame). There are thus several ways of performing self-supervised learning. It depends on the problem, the available sensors and data.
Self-supervised learning can also refer to an unsupervised learning that is used to find features of the unlabelled data (this task is called "representation learning"), which are used to "label" the same unlabelled data.
RL could actually be considered an instance of self-supervised learning, because the inputs (the experience) is used to label the quality of the states (e.g. by estimating the value function) or actions.
SSL has thus slightly different definitions depending on the context.
How are semi and self-supervised learning related? For example, self-supervised learning can be used to construct the labelled dataset that is used in semi-supervised learning.
answered May 12 at 1:16
nbronbro
3,3442826
edited May 12 at 11:27
answered May 12 at 1:16
nbronbro
3,3442826
answered May 12 at 1:16
nbronbro
3,3442826
answered May 12 at 1:16
nbronbro
3,3442826
3,3442826
add a comment |
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