Latent - Revision history

BPeat at 16:27, 16 September 2023

2023-09-16T16:27:47Z

BPeat: /* Latent Spaces & Variables in Generative AI */

2023-09-16T16:22:49Z

‎Latent Spaces & Variables in Generative AI

BPeat: /* Latent Space in Neural Networks */

2023-09-16T16:19:05Z

‎Latent Space in Neural Networks

BPeat at 16:12, 16 September 2023

2023-09-16T16:12:54Z

BPeat at 15:52, 16 September 2023

2023-09-16T15:52:51Z

BPeat at 15:50, 16 September 2023

2023-09-16T15:50:22Z

BPeat at 15:49, 16 September 2023

2023-09-16T15:49:59Z

BPeat at 14:40, 16 September 2023

2023-09-16T14:40:56Z

BPeat at 14:38, 16 September 2023

2023-09-16T14:38:36Z

BPeat: /* Latent Spaces & Variables in Generative AI */

2023-09-16T14:33:07Z

‎Latent Spaces & Variables in Generative AI

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 * [[Latent Dirichlet Allocation (LDA)]]
 * [[Lifelong Latent Actor-Critic (LILAC)]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [[Natural Language Processing (NLP)]] ... [[Natural Language Generation (NLG)|Generation (NLG)]] ... [[Natural Language Classification (NLC)|Classification (NLC)]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding (NLU)]] ... [[Language Translation|Translation]] ... [[Summarization]] ... [[Sentiment Analysis|Sentiment]] ... [[Natural Language Tools & Services|Tools]]

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 ** Example: <i>In text generation, a latent variable can be used to control the tone or style of generated text. By modifying the value of the latent variable, the generative model can produce text in different writing styles (e.g., formal, informal, humorous).</i>
-* <b>Image-to-Image Translation</b>: In tasks like image-to-image translation (e.g., turning a sketch into a realistic image), latent variables can represent specific characteristics of the target image, such as texture, color, or lighting conditions.
+* <b>Image-to-Image Translation</b>: In tasks like [[Video/Image|image-to-image translation]] (e.g., turning a sketch into a realistic image), latent variables can represent specific characteristics of the target image, such as texture, color, or lighting conditions.
 ** Example: <i>A generative model for image-to-image translation could use a latent variable to control the season (e.g., summer, winter) of a generated landscape image, resulting in images with different weather conditions.</i>

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 In deep learning, particularly in techniques like autoencoders and [[Variational Autoencoder (VAE)|variational autoencoders (VAEs)]], there is the concept of a "latent space." This is an abstract, [[Dimensional Reduction|low-dimensional space]] where the model maps input data. This latent space is considered a compressed and meaningful representation of the input data, capturing its essential features.
-* <b>Autoencoders</b>: Autoencoders are neural networks designed for [[Dimensional Reduction|dimensionality reduction]] and feature learning. They consist of two main parts: an encoder and a decoder. The encoder maps the input data to a [[Dimensional Reduction|lower-dimensional]] latent space representation, while the decoder attempts to reconstruct the original data from this representation.
+* <b>[[Autoencoder (AE) / Encoder-Decoder]]</b>: [[Autoencoder (AE) / Encoder-Decoder|Autoencoders]] are neural networks designed for [[Dimensional Reduction|dimensionality reduction]] and feature learning. They consist of two main parts: an encoder and a decoder. The encoder maps the input data to a [[Dimensional Reduction|lower-dimensional]] latent space representation, while the decoder attempts to reconstruct the original data from this representation.
-** Example: <i>In image denoising, an autoencoder can be trained to map noisy images into a [[Dimensional Reduction|lower-dimensional]] latent space and then decode them to produce denoised images. The latent space captures essential image features while removing noise.</i>
+** Example: <i>In image denoising, an [[Autoencoder (AE) / Encoder-Decoder|autoencoder]] can be trained to map noisy images into a [[Dimensional Reduction|lower-dimensional]] latent space and then decode them to produce denoised images. The latent space captures essential image features while removing noise.</i>
-* <b>[[Variational Autoencoder (VAE)|Variational Autoencoders (VAEs)]]</b>: [[Variational Autoencoder (VAE)|VAEs]] are a type of autoencoder that extends the concept of a latent space with probabilistic modeling. In [[Variational Autoencoder (VAE)|VAEs]], the encoder maps input data to a probability distribution in the latent space, typically following a Gaussian distribution. The latent space is sampled to generate data points.
+* <b>[[Variational Autoencoder (VAE)|Variational Autoencoders (VAEs)]]</b>: [[Variational Autoencoder (VAE)|VAEs]] are a type of [[Autoencoder (AE) / Encoder-Decoder|autoencoder] that extends the concept of a latent space with probabilistic modeling. In [[Variational Autoencoder (VAE)|VAEs]], the encoder maps input data to a probability distribution in the latent space, typically following a Gaussian distribution. The latent space is sampled to generate data points.
 ** Example: <i>In generative modeling, [[Variational Autoencoder (VAE)|VAEs]] are used to generate new data samples, such as images or text. The latent space of a [[Variational Autoencoder (VAE)|VAE]] can be manipulated to produce variations of a given input, enabling the generation of diverse and novel data.</i>

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 * [[Probabilistic Latent Semantic Analysis (PLSA)]]
 * [[Latent Dirichlet Allocation (LDA)]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 * [[Natural Language Processing (NLP)]] ... [[Natural Language Generation (NLG)|Generation (NLG)]] ... [[Natural Language Classification (NLC)|Classification (NLC)]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding (NLU)]] ... [[Language Translation|Translation]] ... [[Summarization]] ... [[Sentiment Analysis|Sentiment]] ... [[Natural Language Tools & Services|Tools]]

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 * [[Video/Image]] ... [[Vision]] ... [[Enhancement]] ... [[Fake]] ... [[Reconstruction]] ... [[Colorize]] ... [[Occlusions]] ... [[Predict image]] ... [[Image/Video Transfer Learning]] ... [[Art]] ... [[Photography]]
-The term "latent" refers to something that is not directly observable or explicit but exists as an underlying or hidden representation within the data or a model. Latent variables or features capture essential information that may not be immediately apparent in the raw input data, and they are often learned through various techniques like [[Dimensional Reduction|dimensionality reduction]], [[clustering]], or neural networks.
+The term "latent" refers to something that is not directly observable or explicit but exists as an underlying or hidden representation within the data or a model. Latent variables or features capture essential information that may not be immediately apparent in the raw input data, and they are often learned through various techniques like [[Dimensional Reduction|dimensionality reduction]], [[clustering]], or [[Neural Network|neural networks]].
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	= Latent Spaces & Variables in Generative AI =		= Latent Spaces & Variables in Generative AI =

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	The term "latent" refers to something that is not directly observable or explicit but exists as an underlying or hidden representation within the data or a model. Latent variables or features capture essential information that may not be immediately apparent in the raw input data, and they are often learned through various techniques like [[Dimensional Reduction\|dimensionality reduction]], [[clustering]], or neural networks.		The term "latent" refers to something that is not directly observable or explicit but exists as an underlying or hidden representation within the data or a model. Latent variables or features capture essential information that may not be immediately apparent in the raw input data, and they are often learned through various techniques like [[Dimensional Reduction\|dimensionality reduction]], [[clustering]], or neural networks.
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		+	Latent is an adjective meaning something that is hidden or not obvious, but may develop in the future. For example, you might say that a latent fingerprint is potentially present but not yet visible. Synonyms of latent include hidden, secret, concealed, and invisible.
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	= Latent Spaces & Variables in Generative AI =		= Latent Spaces & Variables in Generative AI =

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 * [[Latent Dirichlet Allocation (LDA)]]
 * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]]
 The term "latent" refers to something that is not directly observable or explicit but exists as an underlying or hidden representation within the data or a model. Latent variables or features capture essential information that may not be immediately apparent in the raw input data, and they are often learned through various techniques like [[Dimensional Reduction|dimensionality reduction]], [[clustering]], or neural networks.
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 ** Example: <i>Given an image of a content and a style image, a neural network can map both images to their respective latent spaces. By mixing the content latent representation with the style latent representation, a new image can be generated that combines the content of one image with the artistic style of another.</i>
-* <b>Word Embeddings and Natural Language Processing</b>: In NLP, word embeddings like Word2Vec and GloVe can be thought of as latent spaces for words. Words are mapped to [[Dimensional Reduction|high-dimensional vectors]] where their semantic meaning and relationships are captured.
+* <b>Word Embeddings and Natural Language Processing</b>: In [[Natural Language Processing (NLP)]], word [[embedding]]s like [[Word2Vec]] and [[Global Vectors for Word Representation (GloVe)]] can be thought of as latent spaces for words. Words are mapped to [[Dimensional Reduction|high-dimensional vectors]] where their semantic meaning and relationships are captured.
-** Example: <i>Word embeddings can be used to find words with similar meanings (e.g., "king" and "queen" are close in the latent space) or perform tasks like sentiment analysis and text classification.</i>
+** Example: <i>Word [[embedding]]s can be used to find words with similar meanings (e.g., "king" and "queen" are close in the latent space) or perform tasks like [[[[Sentiment Analysis|Sentiment]]sentiment analysis]] and [[Natural Language Classification (NLC)|text classification]].</i>
-* <b>Face Recognition and Identity Verification</b>: In face recognition systems, the latent space is often used to represent faces as embeddings. Each face is mapped to a point in this space, and similarity measures are used to determine whether two faces are from the same person.
+* <b>Face Recognition and Identity Verification</b>: In face recognition systems, the latent space is often used to represent faces as [[embedding]]s. Each face is mapped to a point in this space, and similarity measures are used to determine whether two faces are from the same person.
 ** Example: <i>Face recognition technology in smartphones uses latent representations to unlock devices securely and verify the identity of the user.</i>

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 ** Example: <i>In image generation, a [[Variational Autoencoder (VAE)|VAE]] can be trained on a dataset of human faces. The latent space captures variations in facial features (e.g., expressions, hairstyles). By sampling points in this space and decoding them, the [[Variational Autoencoder (VAE)|VAE]] can generate new, realistic faces with diverse attributes.</i>
-* <b>Generative Adversarial Networks (GANs)</b>: GANs are another type of generative model where the generator network learns to produce data that is indistinguishable from real data, while a discriminator network tries to distinguish real from generated data. The latent space in GANs typically consists of random noise vectors.
+* <b>[[Generative Adversarial Network (GAN)|Generative Adversarial Networks (GANs)]]</b>: [[Generative Adversarial Network (GAN)|GANs]] are another type of generative model where the generator network learns to produce data that is indistinguishable from real data, while a discriminator network tries to distinguish real from generated data. The latent space in [[Generative Adversarial Network (GAN)|GANs]] typically consists of random noise vectors.
-** Example: <i>In artistic style [[transfer]], GANs can use a latent space to manipulate the artistic style of an image. By changing points in the latent space, the GAN can create images with different artistic styles while preserving the content.v</i>
+** Example: <i>In artistic style [[transfer]], [[Generative Adversarial Network (GAN)|GANs]] can use a latent space to manipulate the artistic style of an image. By changing points in the latent space, the [[Generative Adversarial Network (GAN)|GAN]] can create images with different artistic styles while preserving the content.</i>
 * <b>Latent Variables in Sequence Generation</b>: In generative models for sequences, such as [[Recurrent Neural Network (RNN)]] or [[transformer]]s, latent variables can be introduced to capture variations in the data that are not explicitly present in the input.