IEEE

Autoencoder Architectures for Low-Rate Sparse Point Cloud Geometry Coding

Abstract: Efficient compression of sparse point cloud geometry remains a critical challenge in 3D content processing, particularly for low-rate scenarios where conventional codecs struggle to maintain ...
Reuters

Exclusive: Google works to erode Nvidia's software advantage with Meta's help

Google's TorchTPU aims to enhance TPU compatibility with PyTorch Google seeks to help AI developers reduce reliance on Nvidia's CUDA ecosystem TorchTPU initiative is part of Google's plan to attract ...
C&EN

Unmixing Autoencoder for Image Reconstruction from Hyperspectral Data

Research Center for Analytical Sciences, Tianjin Key Laboratory of Biosensing and Molecular Recognition, State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, ...
VentureBeat

DeepMind makes big jump toward interpreting LLMs with sparse autoencoders

Large language models (LLMs) have made remarkable progress in recent years. But understanding how they work remains a challenge and scientists at artificial intelligence labs are trying to peer into ...
IEEE

Dual-Constraint Autoencoder and Adaptive Weighted Similarity Spatial Attention for Unsupervised Anomaly Detection

Abstract: Image reconstruction-based methods with autoencoder have been widely used for unsupervised anomaly detection. By training the reconstruction on normal ...
GitHub

No module named 'pytorch_lightning'

File "C:\Users\stable\ComfyUI\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-Stable-Video-Diffusion\nodes.py", line 56, in load_svd_model self.svd_model = load ...
GitHub

PyTorch implementation of Masked AutoEncoder

We have kindly provided the bash script train_pretrain.sh file for pretraining. You can modify some hyperparameters in the script file according to your own needs. python train_pretrain.py --dataset ...