Overview of one stage in PointMLP. Given an input point cloud, PointMLP progressively extracts local features using residual point MLP blocks. In each stage, we first transform the local point using a ...
Abstract: The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward ...
Abstract: Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly ...
You: Have an Apple Silicon Mac (M1, M2, M1 Pro, M1 Max, M1 Ultra) and would like to set it up for data science and machine learning. This repo: Helps you install ...
We have transitioned TorchAudio into a maintenance phase. This process removed some user-facing features. These features were deprecated from TorchAudio 2.8 and removed in 2.9. Our main goals were to ...
This repository contains an efficient implementation of Kolmogorov-Arnold Network (KAN). The original implementation of KAN is available here. The problem is in the sparsification which is claimed to ...
We train latent diffusion models, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through ...
The current implementation is blazing fast! (~5-9x faster than the original release, ~2-4x faster than this concurrent pytorch implementation) What's the secret sauce behind this speedup? Multiple ...
The cnn.py implemented a simple CNN with pytorch. The network consists of two convolutional-ReLU-pooling layer and a fully-connected-softmax layer. The network structure is as following: ...
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