Difference between revisions of "Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)"

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[http://www.youtube.com/results?search_query=Semi+supervised+Generative+Adversarial+network+SSL+GAN YouTube search...]
 
[http://www.youtube.com/results?search_query=Semi+supervised+Generative+Adversarial+network+SSL+GAN YouTube search...]
 
[http://www.google.com/search?q=Semi+supervised+Generative+Adversarial+network+SSL+GAN+machine+learning+ML+artificial+intelligence ...Google search]
 
[http://www.google.com/search?q=Semi+supervised+Generative+Adversarial+network+SSL+GAN+machine+learning+ML+artificial+intelligence ...Google search]
  
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* [[Semi-Supervised]]
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* [[Attention]] Mechanism  ... [[Transformer]] ... [[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
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* [[Context-Conditional Generative Adversarial Network (CC-GAN)]]
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* [[Supervised|Supervised Learning]] ... [[Semi-Supervised]] ... [[Self-Supervised]] ... [[Unsupervised]]
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* [[Generative]] Modeling
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* [[Context-Conditional Generative Adversarial Network (CC-GAN)]]  ... [[Context]]
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* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
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* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
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* [[Imitation Learning (IL)#Conditional Adversarial Latent Model (CALM)|Imitation Learning (IL): Conditional Adversarial Latent Model (CALM)]]
  
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Extending Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels. We train a generative model G and a discriminator D on a dataset with inputs belonging to one of N classes. At training time, D is made to predict which of N+1 classes the input belongs to, where an extra class is added to correspond to the outputs of G. We show that this method can be used to create a more data-efficient classifier and that it allows for generating higher quality samples than a regular GAN. [http://arxiv.org/pdf/1606.01583.pdf Semi-Supervised Learning with Generative Adversarial Networks | Augustus Odena]
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Extending Generative Adversarial Networks (GANs) to the semi-supervised [[context]] by forcing the discriminator network to output class labels. We train a generative model G and a discriminator D on a dataset with inputs belonging to one of N classes. At training time, D is made to predict which of N+1 classes the input belongs to, where an extra class is added to correspond to the outputs of G. We show that this method can be used to create a more data-efficient classifier and that it allows for generating higher quality samples than a regular GAN. [http://arxiv.org/pdf/1606.01583.pdf Semi-Supervised Learning with Generative Adversarial Networks | Augustus Odena]
  
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Latest revision as of 11:49, 16 March 2024

YouTube search... ...Google search

Extending Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels. We train a generative model G and a discriminator D on a dataset with inputs belonging to one of N classes. At training time, D is made to predict which of N+1 classes the input belongs to, where an extra class is added to correspond to the outputs of G. We show that this method can be used to create a more data-efficient classifier and that it allows for generating higher quality samples than a regular GAN. Semi-Supervised Learning with Generative Adversarial Networks | Augustus Odena


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