238 skills found · Page 1 of 8
amaas / Stanford Dl ExProgramming exercises for the Stanford Unsupervised Feature Learning and Deep Learning Tutorial
facebookresearch / DeepclusterDeep Clustering for Unsupervised Learning of Visual Features
PyRetri / PyRetriOpen source deep learning based unsupervised image retrieval toolbox built on PyTorch🔥
coxlab / PrednetCode and models accompanying "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning"
jatinshah / Ufldl TutorialStanford Unsupervised Feature Learning and Deep Learning Tutorial
Sohl-Dickstein / Diffusion Probabilistic ModelsReference implementation for Deep Unsupervised Learning using Nonequilibrium Thermodynamics
ZhaoZhibin / UDTLSource codes for the paper "Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study" published in TIM
CuriousAI / LadderLadder network is a deep learning algorithm that combines supervised and unsupervised learning
KaiyangZhou / Pytorch Vsumm ReinforceUnsupervised video summarization with deep reinforcement learning (AAAI'18)
danluu / UFLDL TutorialDeep Learning and Unsupervised Feature Learning Tutorial Solutions
JohnGiorgi / DeCLUTRThe corresponding code from our paper "DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations". Do not hesitate to open an issue if you run into any trouble!
Huangying-Zhan / Depth VO FeatUnsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction
jwyang / JULE.torchTorch code for our CVPR 2016 paper "Joint Unsupervised LEarning of Deep Representations and Image Clusters"
pathak22 / Unsupervised Video[CVPR 2017] Unsupervised deep learning using unlabelled videos on the web
rinuboney / LadderLadder network is a deep learning algorithm that combines supervised and unsupervised learning.
xiaoaoran / 3d Url Survey(TPAMI2023) Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey
CompPhysics / MachineLearningCourse on Machine Learning and Statistical data Analysis with book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html. Contains Linear and Logistic Regression, Neural Networks and Deep Learning methods, Decision Trees, Random forests, Boosting methods and other ensemble methods, support vector machines and central unsupervised learning algorithms.
vLAR-group / GrowSP🔥GrowSP in PyTorch (CVPR 2023)
abojchevski / Graph2gaussGaussian node embeddings. Implementation of "Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking".
USTCPCS / CVPR2018 AttentionContext Encoding for Semantic Segmentation MegaDepth: Learning Single-View Depth Prediction from Internet Photos LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume On the Robustness of Semantic Segmentation Models to Adversarial Attacks SPLATNet: Sparse Lattice Networks for Point Cloud Processing Left-Right Comparative Recurrent Model for Stereo Matching Enhancing the Spatial Resolution of Stereo Images using a Parallax Prior Unsupervised CCA Discovering Point Lights with Intensity Distance Fields CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation Learning a Discriminative Feature Network for Semantic Segmentation Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation Unsupervised Deep Generative Adversarial Hashing Network Monocular Relative Depth Perception with Web Stereo Data Supervision Single Image Reflection Separation with Perceptual Losses Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains EPINET: A Fully-Convolutional Neural Network for Light Field Depth Estimation by Using Epipolar Geometry FoldingNet: Interpretable Unsupervised Learning on 3D Point Clouds Decorrelated Batch Normalization Unsupervised Learning of Depth and Egomotion from Monocular Video Using 3D Geometric Constraints PU-Net: Point Cloud Upsampling Network Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer Tell Me Where To Look: Guided Attention Inference Network Residual Dense Network for Image Super-Resolution Reflection Removal for Large-Scale 3D Point Clouds PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image Fully Convolutional Adaptation Networks for Semantic Segmentation CRRN: Multi-Scale Guided Concurrent Reflection Removal Network DenseASPP: Densely Connected Networks for Semantic Segmentation SGAN: An Alternative Training of Generative Adversarial Networks Multi-Agent Diverse Generative Adversarial Networks Robust Depth Estimation from Auto Bracketed Images AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation DeepMVS: Learning Multi-View Stereopsis GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation Single-Image Depth Estimation Based on Fourier Domain Analysis Single View Stereo Matching Pyramid Stereo Matching Network A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation Image Correction via Deep Reciprocating HDR Transformation Occlusion Aware Unsupervised Learning of Optical Flow PAD-Net: Multi-Tasks Guided Prediciton-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing Surface Networks Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation TextureGAN: Controlling Deep Image Synthesis with Texture Patches Aperture Supervision for Monocular Depth Estimation Two-Stream Convolutional Networks for Dynamic Texture Synthesis Unsupervised Learning of Single View Depth Estimation and Visual Odometry with Deep Feature Reconstruction Left/Right Asymmetric Layer Skippable Networks Learning to See in the Dark
