[IEEE TMI最佳论文奖] UNet++的正式实施
skip-connections 开源项目推荐(42 个)
skip-connections 话题在 GitHub 上没有填写简介,这里按 Star 数汇总最相关的热门仓库。
热门项目 前 20
按 Star 数排序展示前 20 个 · GitHub 共 42 个仓库🔥使用Deep Image Prior和Pytorch收件箱来验证CNN的水印删除。
[TPAMI 2022]“训练深层图神经网络的技巧袋综合基准研究”作者:陈天龙 *、周凯雄 *、段克宇、郑文青、王培浩、夏胡、王张洋
Codes for ICLR 2020 paper "Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets"
Nested U-Net with two-level skip connections for speech enhancement
Source code (train/test) accompanying the paper entitled "Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach" in CVPR 2019 (https://arxiv.org/abs/1903.10764).
ResNet-34 Model trained from scratch to classify 450 different species of birds with 98.6% accuracy.
Abdominal Organ Segmentation using Multi Decoder Network (MDNet) [Accepted at ICASSP 2025]
Code for the IEEE PIMRC 2023 paper "Radio Map Estimation with Deep Dual Path Autoencoders and Skip Connection Learning". Radio-map reconstruction with deep autoencoders.
The project presents a comparative study of Brain Tumor Segmentation using 3 approaches - 1) Sobel Operator and U-Net, 2) V-Net, 3) W-Net
Implementations of different variations of U-net - adding deconv layers, dense net variant and including skip connections
Graduation Project. Applying Generative Adversarial Networks(GAN) with Residual-In-Residual(RIR) blocks.
This is the official implementation of "Novel View Synthesis with Skip Connections" (ICIP 2020)
This project demonstrates the implementation of ResNet50 from scratch and its application for chest cancer classification using the Chest CT-scan Images dataset.
IEEE paper implementation of Single-View 2D-3D Reconstruction.
The main goal of this project is to come up with an architecture having the highest test accuracy on the CIFAR-10 image classification dataset, under the constraint that model has no more than 5 million parameters.
Reconstructing Medical Images using Generative model.
Replication of Jasper speech-to-text network using Intel optimized TensorFlow.
基于PyTorch U-Net的图像边缘分割系统,支持自定义数据集训练、推理与评估,适用于工业检测、医学影像和遥感影像边缘提取
Code accompanying the paper: "Hybrid Skip: A Biologically Inspired Skip Connection for the UNet Architecture"
关于这个话题
- 清单取自 GitHub 话题页并按 Star 数排序,每次展示前 20 个项目;原始页面见 https://github.com/topics/skip-connections。
- 该话题在 GitHub 上共有 42 个仓库, 本站收录的是其中最热门的 20 个,不是全量列表。
- Star / Fork 每日自动刷新一次,实时数据以 GitHub 为准。
- 当前 20 个项目累计 4201 Star,覆盖 2 种主要语言,数据更新于 2026-11-10。
- 中文介绍来自 GitHub 官方话题说明(github/explore)的机器翻译,可点「查看原文」对照;译文缺失时会先展示原文。