Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more expressive variational family, the inverse ...

Abstract: For taking out the adjustment process of sparse auto-encoder for broad learning system, Feng et al. proposed fuzzy broad learning system by replacing the feature nodes of broad learning ...

Same as traditional autoencoders, VAE architecture has two parts: an encoder and a decoder. Traditional AE models map inputs into a latent-space vector and reconstruct the output from this vector. VAE ...

Abstract: Anomaly detection has a wide range of applications in security area such as network monitoring and smart city/campus construction. It has become an active research issue of great concern in ...

We propose a direct domain adaptation (DDA) approach to enrich the training of supervised neural networks on synthetic data by features from real-world data. The process involves a series of linear ...

A spiking neural network model inspired by synaptic pruning is developed and trained to extract features of hand-written digits. The network is composed of three spiking neural layers and one output ...

In this project, I built a model to perform handwritten digit string recognition using synthetic data generated by concatenating digits from the MNIST dataset. Different overlapping rates and paddings ...

Dr. James McCaffrey of Microsoft Research provides full code and step-by-step examples of anomaly detection, used to find items in a dataset that are different from the majority for tasks like ...