rinuboney / Ladder
Licence: mit
Ladder network is a deep learning algorithm that combines supervised and unsupervised learning.
Stars: ✭ 242
Programming Languages
python
139335 projects - #7 most used programming language
Projects that are alternatives of or similar to Ladder
Fleetx
Paddle Distributed Training Extended. 飞桨分布式训练扩展包
Stars: ✭ 196 (-19.01%)
Mutual labels: unsupervised-learning
Lkvolearner
Learning Depth from Monocular Videos using Direct Methods, CVPR 2018
Stars: ✭ 210 (-13.22%)
Mutual labels: unsupervised-learning
Contrastive Predictive Coding Pytorch
Contrastive Predictive Coding for Automatic Speaker Verification
Stars: ✭ 223 (-7.85%)
Mutual labels: unsupervised-learning
Simclr
A PyTorch implementation of SimCLR based on ICML 2020 paper "A Simple Framework for Contrastive Learning of Visual Representations"
Stars: ✭ 198 (-18.18%)
Mutual labels: unsupervised-learning
Interactive Deep Colorization
Deep learning software for colorizing black and white images with a few clicks.
Stars: ✭ 2,465 (+918.6%)
Mutual labels: deep-learning-algorithms
Pytorch Byol
PyTorch implementation of Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning
Stars: ✭ 213 (-11.98%)
Mutual labels: unsupervised-learning
Free Ai Resources
🚀 FREE AI Resources - 🎓 Courses, 👷 Jobs, 📝 Blogs, 🔬 AI Research, and many more - for everyone!
Stars: ✭ 192 (-20.66%)
Mutual labels: unsupervised-learning
Recursive Cascaded Networks
[ICCV 2019] Recursive Cascaded Networks for Unsupervised Medical Image Registration
Stars: ✭ 237 (-2.07%)
Mutual labels: unsupervised-learning
Gemsec
The TensorFlow reference implementation of 'GEMSEC: Graph Embedding with Self Clustering' (ASONAM 2019).
Stars: ✭ 210 (-13.22%)
Mutual labels: unsupervised-learning
Transmomo.pytorch
This is the official PyTorch implementation of the CVPR 2020 paper "TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting".
Stars: ✭ 225 (-7.02%)
Mutual labels: unsupervised-learning
Keras deep clustering
How to do Unsupervised Clustering with Keras
Stars: ✭ 202 (-16.53%)
Mutual labels: unsupervised-learning
Awesome Deep Learning And Machine Learning Questions
【不定期更新】收集整理的一些网站中（如知乎、Quora、Reddit、Stack Exchange等）与深度学习、机器学习、强化学习、数据科学相关的有价值的问题
Stars: ✭ 203 (-16.12%)
Mutual labels: deep-learning-algorithms
Spectralcluster
Python re-implementation of the spectral clustering algorithm in the paper "Speaker Diarization with LSTM"
Stars: ✭ 220 (-9.09%)
Mutual labels: unsupervised-learning
Variational Ladder Autoencoder
Implementation of VLAE
Stars: ✭ 196 (-19.01%)
Mutual labels: unsupervised-learning
Unsupervised Video
[CVPR 2017] Unsupervised deep learning using unlabelled videos on the web
Stars: ✭ 233 (-3.72%)
Mutual labels: unsupervised-learning
Pixelnet
The repository contains source code and models to use PixelNet architecture used for various pixel-level tasks. More details can be accessed at <http://www.cs.cmu.edu/~aayushb/pixelNet/>.
Stars: ✭ 194 (-19.83%)
Mutual labels: unsupervised-learning
Gan Sandbox
Vanilla GAN implemented on top of keras/tensorflow enabling rapid experimentation & research. Branches correspond to implementations of stable GAN variations (i.e. ACGan, InfoGAN) and other promising variations of GANs like conditional and Wasserstein.
Stars: ✭ 210 (-13.22%)
Mutual labels: unsupervised-learning
Unflow
UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss
Stars: ✭ 239 (-1.24%)
Mutual labels: unsupervised-learning
Colorization
Automatic colorization using deep neural networks. "Colorful Image Colorization." In ECCV, 2016.
Stars: ✭ 2,791 (+1053.31%)
Mutual labels: deep-learning-algorithms
Machine Learning Notebooks
Machine Learning notebooks for refreshing concepts.
Stars: ✭ 222 (-8.26%)
Mutual labels: deep-learning-algorithms
This is an implementation of Ladder Network in TensorFlow. Ladder network is a deep learning algorithm that combines supervised and unsupervised learning. It was introduced in the paper Semi-Supervised Learning with Ladder Network by A Rasmus, H Valpola, M Honkala, M Berglund, and T Raiko.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].