Difference between revisions of "Self-Supervised"

Revision as of 10:42, 8 December 2019

Supervised Learning
Unsupervised Learning

Self-supervised learning refers to an unsupervised learning problem that is framed as a supervised learning problem in order to apply supervised learning algorithms to solve it. Supervised learning algorithms are used to solve an alternate or pretext task, the result of which is a model or representation that can be used in the solution of the original (actual) modeling problem. A common example of self-supervised learning is computer vision where a corpus of unlabeled images is available and can be used to train a supervised model, such as making images grayscale and having a model predict a color representation (colorization) or removing blocks of the image and have a model predict the missing parts (inpainting). 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery

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-Self-supervised learning refers to an unsupervised learning problem that is framed as a supervised learning problem in order to apply supervised learning algorithms to solve it.
+Self-supervised learning refers to an unsupervised learning problem that is framed as a supervised learning problem in order to apply supervised learning algorithms to solve it. Supervised learning algorithms are used to solve an alternate or pretext task, the result of which is a model or representation that can be used in the solution of the original (actual) modeling problem. A common example of self-supervised learning is computer vision where a corpus of unlabeled images is available and can be used to train a supervised model, such as making images grayscale and having a model predict a color representation (colorization) or removing blocks of the image and have a model predict the missing parts (inpainting). [http://machinelearningmastery.com/types-of-learning-in-machine-learning/ 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery]
-Supervised learning algorithms are used to solve an alternate or pretext task, the result of which is a model or representation that can be used in the solution of the original (actual) modeling problem.
+* [[Autoencoder (AE) / Encoder-Decoder]]
+* [[Generative Adversarial Network (GAN)]]
-A common example of self-supervised learning is computer vision where a corpus of unlabeled images is available and can be used to train a supervised model, such as making images grayscale and having a model predict a color representation (colorization) or removing blocks of the image and have a model predict the missing parts (inpainting).
-== [[Autoencoder (AE) / Encoder-Decoder]] ==
-A general example of self-supervised learning algorithms are autoencoders. These are a type of neural network that is used to create a compact or compressed representation of an input sample. They achieve this via a model that has an encoder and a decoder element separated by a bottleneck that represents the internal compact representation of the input.
-These autoencoder models are trained by providing the input to the model as both input and the target output, requiring that the model reproduce the input by first encoding it to a compressed representation then decoding it back to the original. Once trained, the decoder is discarded and the encoder is used as needed to create compact representations of input.
-Although autoencoders are trained using a supervised learning method, they solve an unsupervised learning problem, namely, they are a type of projection method for reducing the dimensionality of input data.
-== Generative Adversarial Network (GAN) ==
-Another example of self-supervised learning is generative adversarial networks, or GANs. These are generative models that are most commonly used for creating synthetic photographs using only a collection of unlabeled examples from the target domain.
-GAN models are trained indirectly via a separate discriminator model that classifies examples of photos from the domain as real or fake (generated), the result of which is fed back to update the GAN model and encourage it to generate more realistic photos on the next iteration.  [http://machinelearningmastery.com/types-of-learning-in-machine-learning/ 14 Different Types of Learning in Machine Learning | Jason Brownlee - Machine Learning Mastery]

Difference between revisions of "Self-Supervised"

Revision as of 10:42, 8 December 2019

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