GitHub

sofia-dominguezc/MNIST-diffusion

Diffusion model implementation in pytorch trained on the latent space of an autoencoder. I built this as a personal project to answer a question: what is the smallest model that can still generate ...
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

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 ...
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 ...
Europe PMC

Harnessing unlabeled data: Enhanced rare earth component content prediction based on BiLSTM-Deep autoencoder.

Traditional data-driven models for predicting rare earth component content are primarily developed by relying on supervised learning methods, which suffer from limitations such as a lack of labeled ...