ALAE, or Adversarial Latent Autoencoder, is an innovative type of autoencoder used to tackle some of the limitations of generative adversarial networks. The architecture employed by ALAE allows the machine to learn the latent distribution directly from data. This means that it can address entanglement, which is a common problem with other approaches.
Advantages of ALAE
ALAE has several advantages over other generative models. Firstly, it retains the generative properties of GANs, which makes it useful for building up recent advances in this area. Secondly, it can include independent sources of stochasticity, which have shown to be vital for generating image details. Thirdly, recent improvements on GAN loss functions, regularization, and hyperparameters tuning can all be exploited using ALAE.
The Working Principle of ALAE
ALAE is named as such because of how it operates. Rather than autoencoding the data space, it works on the latent space. Therefore, AE reciprocity is imposed in the latent space to implement both A and B. This strategy avoids using reconstruction losses based on a simple $\mathcal{l}\){2}$ norm that operates in data space, where they are often suboptimal, like in the case of image space.
ALAE Architecture
The architecture employed by ALAE is complex but relatively simple to grasp. It comprises of three blocks: the encoder, the decoder, and the discriminator. Here's a look at each:
- The Encoder: The encoder maps input data to a fixed compact latent representation. In simple terms, it takes the data and compresses it so the machine can identify the unique features of the input data efficiently.
- The Decoder: The decoder maps the compact latent representation back to the original input space. Here, it reverses the process done by the encoder to reconstruct the original input space from the compressed representation.
- The Discriminator: The discriminator is responsible for distinguishing between real and fake compressed representations. It compares the output of the encoder with a sample of the real-world dataset to determine the accuracy of the compressed representation.
Overall, ALAE is a powerful tool in machine learning that can solve many of the problems faced by other approaches. Its advancements in loss functions, regularization, and parameter tuning have made it a popular choice for researchers looking to generate realistic images.
