GitHub

latent_diffusion_mnist_ddpm_using_autoencoder.py

In this notebook, we will implement an **Autoencoder** with Convolutional Attention Blocks (CABs) to encode and decode MNIST digits, aiming to learn efficient latent representations. To get started, ...
Visual Studio Magazine

Generating Synthetic Data Using a Variational Autoencoder with PyTorch

Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...
GitHub

Simplified PyTorch Implementation of Improved Autoencoder Model With Memory Module for Anomaly Detection

Improved Autoencoder Model With Memory Module for Anomaly Detection (IAEMM) is an unsupervised anomaly detection algorithm that enhances traditional autoencoders with a memory module and a hypersphere ...
IEEE

Cross-Modal Contrastive Masked AutoEncoder for Compressed Video Pre-Training

Abstract: In this paper, we propose a novel Transformer based approach, namely Cross-modal Contrastive Masked AutoEncoder (C2MAE), to Self-Supervised Learning (SSL) on compressed videos. A unified ...
IEEE

MMEVAE: A Time-Varying Rolling Bearing Data Enhancement Method Based on a Multimodal Mechanism Enhanced Variational Autoencoder and Self-Attention Module

Abstract: Variational autoencoder (VAE) is widely used as a data enhancement technique. However, it faces challenges with inaccurate potential spatial distribution and poor reconstruction quality when ...
marktechpost

This AI Paper Introduces a Short KL+MSE Fine-Tuning Strategy: A Low-Cost Alternative to End-to-End Sparse Autoencoder Training for Interpretability

Sparse autoencoders are central tools in analyzing how large language models function internally. Translating complex internal states into interpretable components allows researchers to break down ...