Mar 1, 2025 · Autoencoders consists of two components: Encoder: This compresses the input into a compact representation and capture the most relevant features. Decoder: It reconstructs the input data from this compressed form to make it as similar as possible to the original input.

14.4 Stochastic Encoders and Decoders ¶ Given a hidden code h, we may think of the decoder as providing a conditional distribution pdecoder(x|h) p d e c o d e r (x | h). We may train the autoencoder by minimizing −lpgPdecoder(x|h) − l p g P d e c o d e r (x | h). x is Gaussian, negative log-likehood yield mean squared error

Mar 10, 2021 · Encoder-Decoder models are jointly trained to maximize the conditional probabilities of the target sequence given the input sequence. How the Sequence to …

Autoencoder with a one-dimensional code and a very powerful nonlinear encoder can learn to map x(i) to code i. 2. Regularized Autoencoder Properties. The log pmodel(h) term can be sparsity-inducing. For example the Laplace prior. 3. Representational Power, Layer Size and Depth. 4. Stochastic Encoders and Decoders.

Stochastic Autoencoders pencoder (h h x) pdecoder (x | h) r Figure 14.2 of a stochastic autoencoder, in which both mo with correlations. Figure 14.2: The structure decoder are not simple functions but instead involve some noise

In this paper we provide one illustrative example which shows that stochastic encoders can signifi-cantly outperform the best deterministic encoders. Our toy example suggests that stochastic encoders may be particularly useful in the regime of “perfect perceptual quality”.

Given a hidden code h, we may think of the decoder as providing a conditional distribution p_ {decoder} (x|h). We may train the autoencoder by minimizing -lpg P_ {decoder} (x|h).

Encoder-decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for sequence-to-sequence problems such as machine translation. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape.

What about nonlinear encoder and decoder? Special case of energy model. Take 3 hidden layers and ignore bias: .

One of the popular approaches in generative modeling is Variational autoencoder (VAE), which combines deep learning with statistical inference by making a strong distribution assumption on h ~ P (h), such as Gaussian or Bernoulli.