Context-Conditional Generative Adversarial Network (CC-GAN) - Revision history

BPeat at 22:13, 28 April 2024

2024-04-28T22:13:04Z

BPeat: Text replacement - "* Conversational AI ... ChatGPT | OpenAI ... Bing | Microsoft ... Bard | Google ... Claude | Anthropic ... Perplexity ... You ... Ernie | Baidu" to "* Conversational AI ... [[C...

2024-03-16T15:54:09Z

BPeat at 16:27, 16 September 2023

2023-09-16T16:27:39Z

BPeat at 19:29, 2 September 2023

2023-09-02T19:29:50Z

BPeat at 11:51, 13 August 2023

2023-08-13T11:51:10Z

BPeat at 11:50, 13 August 2023

2023-08-13T11:50:44Z

BPeat at 11:06, 13 August 2023

2023-08-13T11:06:04Z

BPeat at 11:02, 13 August 2023

2023-08-13T11:02:49Z

BPeat at 10:42, 13 August 2023

2023-08-13T10:42:15Z

BPeat at 00:39, 14 July 2023

2023-07-14T00:39:30Z

@@ Line 21: / Line 21: @@
 * [[Attention]] Mechanism  ... [[Transformer]] ... [[Generative Pre-trained Transformer (GPT)]] ... [[Generative Adversarial Network (GAN)|GAN]] ... [[Bidirectional Encoder Representations from Transformers (BERT)|BERT]]
 * [[Supervised|Supervised Learning]] ... [[Semi-Supervised]] ... [[Self-Supervised]] ... [[Unsupervised]]
 * [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]]

@@ Line 25: / Line 25: @@
 * [[Feature Exploration/Learning]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
-* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
+* [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]]
 * [[Imitation Learning (IL)#Conditional Adversarial Latent Model (CALM)|Imitation Learning (IL): Conditional Adversarial Latent Model (CALM)]]
 ** [[Latent]]

@@ Line 27: / Line 27: @@
 * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
 * [[Imitation Learning (IL)#Conditional Adversarial Latent Model (CALM)|Imitation Learning (IL): Conditional Adversarial Latent Model (CALM)]]
 A method for harnessing unlabeled image data based on image in-painting. A generative model is trained to generate pixels within a missing hole, based on the [[context]] provided by surrounding parts of the image. These in-painted images are then used in an adversarial setting (Goodfellow et al., 2014) to train a large discriminator model whose task is to determine if the image was real (from the unlabeled training set) or fake (an in-painted image). The realistic looking fake examples provided by the generative model cause the discriminator to learn features that generalize to the related task of classifying objects. Thus adversarial training for the in-painting task can be used to regularize large discriminative models during supervised training on a handful of labeled images. Independently developed [[context]]-encoder approach of Pathak et al. (2016). This introduces an encoder-decoder framework, that is used to in-paint images where a patch has been randomly removed. [https://arxiv.org/pdf/1611.06430.pdf Semi-Supervised Learning with [[Context]]-Conditional Generative Adversarial Networks | Emily Denton, Sam Gross, Rob Fergus]

@@ Line 24: / Line 24: @@
 * [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]]
 * [[Feature Exploration/Learning]]
-* [[Generative AI]] ... [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
+* [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [[Imitation Learning (IL)#Conditional Adversarial Latent Model (CALM)|Imitation Learning (IL): Conditional Adversarial Latent Model (CALM)]]

@@ Line 25: / Line 25: @@
 * [[Feature Exploration/Learning]]
 * [[Generative AI]] ... [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
-* [[Imitation Learning (IL)#Conditional Adversarial Latent Model (CALM)|Imitation Learning: Conditional Adversarial Latent Model (CALM)]]
+* [[Imitation Learning (IL)#Conditional Adversarial Latent Model (CALM)|Imitation Learning (IL): Conditional Adversarial Latent Model (CALM)]]
 A method for harnessing unlabeled image data based on image in-painting. A generative model is trained to generate pixels within a missing hole, based on the [[context]] provided by surrounding parts of the image. These in-painted images are then used in an adversarial setting (Goodfellow et al., 2014) to train a large discriminator model whose task is to determine if the image was real (from the unlabeled training set) or fake (an in-painted image). The realistic looking fake examples provided by the generative model cause the discriminator to learn features that generalize to the related task of classifying objects. Thus adversarial training for the in-painting task can be used to regularize large discriminative models during supervised training on a handful of labeled images. Independently developed [[context]]-encoder approach of Pathak et al. (2016). This introduces an encoder-decoder framework, that is used to in-paint images where a patch has been randomly removed. [https://arxiv.org/pdf/1611.06430.pdf Semi-Supervised Learning with [[Context]]-Conditional Generative Adversarial Networks | Emily Denton, Sam Gross, Rob Fergus]

@@ Line 14: / Line 14: @@
 </script>
 }}
-[https://www.youtube.com/results?search_query=Context+Conditional+Generative+Adversarial+network+CC+GAN YouTube search...]
+[https://www.youtube.com/results?search_query=ai+Context+Conditional+Generative+Adversarial+network+CC+GAN YouTube]
-[https://www.google.com/search?q=Context+Conditional+Generative+Adversarial+network+CC+GAN+machine+learning+ML+artificial+intelligence ...Google search]
+[https://www.quora.com/search?q=ai%20Context%20Conditional%20Generative%20Adversarial%20network%20CC%20GAN ... Quora]
 * [[Supervised|Supervised Learning]] ... [[Semi-Supervised]] ... [[Self-Supervised]] ... [[Unsupervised]]
@@ Line 21: / Line 24: @@
 * [[Feature Exploration/Learning]]
 * [[Generative AI]] ... [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
 A method for harnessing unlabeled image data based on image in-painting. A generative model is trained to generate pixels within a missing hole, based on the [[context]] provided by surrounding parts of the image. These in-painted images are then used in an adversarial setting (Goodfellow et al., 2014) to train a large discriminator model whose task is to determine if the image was real (from the unlabeled training set) or fake (an in-painted image). The realistic looking fake examples provided by the generative model cause the discriminator to learn features that generalize to the related task of classifying objects. Thus adversarial training for the in-painting task can be used to regularize large discriminative models during supervised training on a handful of labeled images. Independently developed [[context]]-encoder approach of Pathak et al. (2016). This introduces an encoder-decoder framework, that is used to in-paint images where a patch has been randomly removed. [https://arxiv.org/pdf/1611.06430.pdf Semi-Supervised Learning with [[Context]]-Conditional Generative Adversarial Networks | Emily Denton, Sam Gross, Rob Fergus]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=Context+Conditional+Generative+Adversarial+network+CC+GAN YouTube search...]
 [https://www.google.com/search?q=Context+Conditional+Generative+Adversarial+network+CC+GAN+machine+learning+ML+artificial+intelligence ...Google search]
-* [[Semi-Supervised]]
+* [[Supervised|Supervised Learning]] ... [[Semi-Supervised]] ... [[Self-Supervised]] ... [[Unsupervised]]
 * [[Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)]]
 * [[Feature Exploration/Learning]]
-* [[Generative AI]]  ... [[Conversational AI]] ... [[OpenAI]]'s [[ChatGPT]] ... [[Perplexity]]  ... [[Microsoft]]'s [[Bing]] ... [[You]] ...[[Google]]'s [[Bard]] ... [[Baidu]]'s [[Ernie]]
+* [[Generative AI]] ... [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing]] | [[Microsoft]] ... [[Bard]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[Ernie]] | [[Baidu]]
 A method for harnessing unlabeled image data based on image in-painting. A generative model is trained to generate pixels within a missing hole, based on the [[context]] provided by surrounding parts of the image. These in-painted images are then used in an adversarial setting (Goodfellow et al., 2014) to train a large discriminator model whose task is to determine if the image was real (from the unlabeled training set) or fake (an in-painted image). The realistic looking fake examples provided by the generative model cause the discriminator to learn features that generalize to the related task of classifying objects. Thus adversarial training for the in-painting task can be used to regularize large discriminative models during supervised training on a handful of labeled images. Independently developed [[context]]-encoder approach of Pathak et al. (2016). This introduces an encoder-decoder framework, that is used to in-paint images where a patch has been randomly removed. [https://arxiv.org/pdf/1611.06430.pdf Semi-Supervised Learning with [[Context]]-Conditional Generative Adversarial Networks | Emily Denton, Sam Gross, Rob Fergus]