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the sliced Wasserstein distance used by S Kolouri, et al. in their VAE [23] the energy distance implemented in the Radon Sobolev Variational Auto-Encoder [24] the Maximum Mean Discrepancy distance used in the MMD-VAE [25] the Wasserstein distance used in the WAEs [26] kernel-based distances used in the Kernelized Variational Autoencoder (K-VAE ...
The encoder part of the VAE takes an image as input and outputs a lower-dimensional latent representation of the image. This latent representation is then used as input to the U-Net. Once the model is trained, the encoder is used to encode images into latent representations, and the decoder is used to decode latent representations back into images.
The encoder-decoder architecture, often used in natural language processing and neural networks, can be scientifically applied in the field of SEO (Search Engine Optimization) in various ways: Text Processing : By using an autoencoder, it's possible to compress the text of web pages into a more compact vector representation.
The reparameterization trick (aka "reparameterization gradient estimator") is a technique used in statistical machine learning, particularly in variational inference, variational autoencoders, and stochastic optimization.
Stable Diffusion consists of 3 parts: the variational autoencoder (VAE), U-Net, and an optional text encoder. [15] The VAE encoder compresses the image from pixel space to a smaller dimensional latent space , capturing a more fundamental semantic meaning of the image. [ 14 ]
I've tried for a couple of hours to improve the clarity of the central idea of a VAE, but I'm not satisfied with my efforts. In particular, it is still unclear to me whether both the encoder and decoder are technically random, whether any randomness should be added in the decoder, or what (beyond Z) is modeled with a multimodal Gaussian in the ...
The image decoder is trained to convert CLIP image encodings back to images. During inference, a text is converted by the CLIP text encoder to a vector, then it is converted by the prior model to an image encoding, then it is converted by the image decoder to an image. Sora (2024-02) is a diffusion Transformer model (DiT).
Seq2seq RNN encoder-decoder with attention mechanism, training Seq2seq RNN encoder-decoder with attention mechanism, training and inferring The attention mechanism is an enhancement introduced by Bahdanau et al. in 2014 to address limitations in the basic Seq2Seq architecture where a longer input sequence results in the hidden state output of ...