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オートエンコーダ Autoencoder【 AI用語解説】

オートエンコーダ Autoencoder【 AI用語解説】

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AI • Machine Learning • Tech on Instagram: "In autoencoders, the latent space is the compressed, lower dimensional representation of the input data. The encoder part of the autoencoder maps the high-dimensional input into this compact latent space, capturing its most essential features. The organization of this space is typically structured so that similar inputs are located closer together, making reconstruction easier for the decoder. This allows the model to generalize well and find patterns

AI • Machine Learning • Tech on Instagram: "In autoencoders, the latent space is the compressed, lower dimensional representation of the input data. The encoder part of the autoencoder maps the high-dimensional input into this compact latent space, capturing its most essential features. The organization of this space is typically structured so that similar inputs are located closer together, making reconstruction easier for the decoder. This allows the model to generalize well and find patterns

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変分オートエンコーダ Variational Autoencoder, VAE 1【 AI用語解説】

変分オートエンコーダ Variational Autoencoder, VAE 1【 AI用語解説】

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Convolutional Autoencoder Latent Space Visualization - Untuk materi ngajar

Convolutional Autoencoder Latent Space Visualization - Untuk materi ngajar

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AI • Machine Learning • Tech on Instagram: "A Variational Autoencoder (VAE) is a type of neural network (specifically an autoencoder) used for unsupervised learning that uses probability to generate images. Like any other autoencoder, it learns to encode data into a compressed latent space and then decode it back into its original form. The training process involves two main components: an encoder, which maps input data to a probabilistic distribution in the latent space, and a decoder, which re

AI • Machine Learning • Tech on Instagram: "A Variational Autoencoder (VAE) is a type of neural network (specifically an autoencoder) used for unsupervised learning that uses probability to generate images. Like any other autoencoder, it learns to encode data into a compressed latent space and then decode it back into its original form. The training process involves two main components: an encoder, which maps input data to a probabilistic distribution in the latent space, and a decoder, which re

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Artificial Intelligence | AI on Instagram: "Variational Autoencoders (VAEs) are generative models based on autoencoders that generate new, similar images by encoding a reference image into a latent space and then sampling (near the image) from that space. The process goes like this: The VAE encodes the image into a lower dimensional latent representation. New images can now be generated by sampling near this representation and decoding it back into the image space (producing a visible image), pr

Artificial Intelligence | AI on Instagram: "Variational Autoencoders (VAEs) are generative models based on autoencoders that generate new, similar images by encoding a reference image into a latent space and then sampling (near the image) from that space. The process goes like this: The VAE encodes the image into a lower dimensional latent representation. New images can now be generated by sampling near this representation and decoding it back into the image space (producing a visible image), pr

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Artificial Intelligence | AI on Instagram: "Variational Autoencoders (VAEs) are generative models based on autoencoders that generate new, similar images by encoding a reference image into a latent space and then sampling (near the image) from that space. The process goes like this: The VAE encodes the image into a lower dimensional latent representation. New images can now be generated by sampling near this representation and decoding it back into the image space (producing a visible image), pr

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Artificial Intelligence | AI on Instagram: "A Variational Autoencoder (VAE) is a type of neural network (specifically an autoencoder) used for unsupervised learning that uses probability to generate images. Like any other autoencoder, it learns to encode data into a compressed latent space and then decode it back into its original form. The training process involves two main components: an encoder, which maps input data to a probabilistic distribution in the latent space, and a decoder, which re

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Artificial Intelligence | AI on Instagram: "In autoencoders, the latent space is the compressed, lower dimensional representation of the input data. The encoder part of the autoencoder maps the high-dimensional input into this compact latent space, capturing its most essential features. The organization of this space is typically structured so that similar inputs are located closer together, making reconstruction easier for the decoder. This allows the model to generalize well and find patterns

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AI • Machine Learning • Tech on Instagram: "The reparameterization trick is a clever technique used in Variational Autoencoders (VAEs) to make the training process possible using standard gradient-based optimization. Normally, VAEs learn to generate data by first learning a distribution (like a Gaussian) from which to sample, but sampling from this distribution means we can’t backpropagate the loss to update the model (we can’t differentiate a sampling process!). The reparameterization trick sol

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