david-knigge/ccnn
Code repository of the paper "Modelling Long Range Dependencies in ND: From Task-Specific to a General Purpose CNN" https://arxiv.org/abs/2301.10540.
This project offers a new type of Convolutional Neural Network (CNN) architecture called the Continuous Convolutional Neural Network (CCNN). It allows researchers and practitioners to process various types of data, like sequences, images, or 3D point clouds, using a single model without needing to modify its structure. The CCNN takes in diverse raw data and outputs high-performance predictions, often outperforming existing specialized models, making it ideal for machine learning researchers and data scientists.
183 stars. No commits in the last 6 months.
Use this if you need a flexible CNN model that can handle different data types (1D, 2D, 3D) and resolutions without requiring custom architectural changes for each task.
Not ideal if you are looking for a pre-trained, ready-to-use model for a very specific task, as this project focuses on the underlying architecture for building such models.
Stars
183
Forks
21
Language
Python
License
MIT
Category
Last pushed
May 11, 2025
Commits (30d)
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