Abstract: Autoencoders have proven successful across diverse applications such as data reconstruction, anomaly detection, and feature extraction, however, these advancements remain largely dispersed ...

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...

CUDA_VISIBLE_DEVICES=0 python scripts/sample.py -d kitti -r models/lidm/kitti/[model_name]/model.ckpt -n 2000 --eval Besides, to train your own LiDAR Diffusion Models ...

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State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China State Environmental Protection Key Laboratory of Sources ...

Variational AutoEncoders (VAE) have grown in popularity due to their scalability and computational efficiency. It is widely used in voice modeling, clustering, and data augmentation applications. This ...

"In this demo, we build a simple autoencoder using PyTorch. A separate encoder and decoder are built. The encoder is trained to encode the input data into a latent space. The decoder is trained to ...