Jan 11, 2022 · We will start with a general introduction to autoencoders, and we will discuss the role of the activation function in the output layer and the loss function. We will then discuss …

This paper shows that these capsules are quite easy to learn from pairs of transformed images if the neural net has direct, non- visual access to the transformations.

We propose using k-sparse autoencoders [Makhzani and Frey, 2013] to directly control sparsity, simplifying tuning and improving the reconstruction-sparsity frontier. Additionally, we find …

For use in the synthesis planner and beyond, we want to build an autoencoder for Molgraphs (Figure 2). This paper evaluates existing autoencoding techniques as applied to the task of …

Jan 11, 2022 · We will start with a general introduction to autoencoders, and we will discuss the role of the activation function in the output layer and the loss function. We will then discuss …

May 1, 2023 · First, this paper explains the principle of a conventional autoencoder and investigates the primary development process of an autoencoder. Second, We proposed a …

Nov 28, 2023 · Figure 8.2: Autoencoder structure, showing the encoder (left half, light green), and the decoder (right half, light blue), encoding inputs x to the representation a , and decoding the …

As different images need different sized code based on their complex-ity, we propose an autoencoder architecture with a variable sized latent vector. We propose an attention based …

Autoencoders are simple learning circuits which aim to transform inputs into outputs with the least possible amount of distortion. While conceptually simple, they play an important role in …

this paper the denoising autoencoder is considered. It’s trained to reconstruct images with additive gaussian noise with mean of 0 and standard deviation of 0.3 into original ones (Figure 9).