back2futureUnsupervised Learning of Multi-Frame Optical Flow with Occlusions
Stars: ✭ 39 (-87.77%)
ArflowThe official PyTorch implementation of the paper "Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation".
Stars: ✭ 134 (-57.99%)
deepOFTensorFlow implementation for "Guided Optical Flow Learning"
Stars: ✭ 26 (-91.85%)
Back2future.pytorchUnsupervised Learning of Multi-Frame Optical Flow with Occlusions
Stars: ✭ 104 (-67.4%)
VoxelmorphUnsupervised Learning for Image Registration
Stars: ✭ 1,057 (+231.35%)
Hidden Two StreamCaffe implementation for "Hidden Two-Stream Convolutional Networks for Action Recognition"
Stars: ✭ 179 (-43.89%)
CcCompetitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation
Stars: ✭ 348 (+9.09%)
DdflowDDFlow: Learning Optical Flow with Unlabeled Data Distillation
Stars: ✭ 101 (-68.34%)
UnflowUnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Loss
Stars: ✭ 239 (-25.08%)
GuidedNetCaffe implementation for "Guided Optical Flow Learning"
Stars: ✭ 28 (-91.22%)
PCLNetUnsupervised Learning for Optical Flow Estimation Using Pyramid Convolution LSTM.
Stars: ✭ 29 (-90.91%)
deep learningDeep-learning approaches to object recognition from 3D data
Stars: ✭ 19 (-94.04%)
learning-topology-synthetic-dataTensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)
Stars: ✭ 22 (-93.1%)
uctfUnsupervised Controllable Text Generation (Applied to text Formalization)
Stars: ✭ 19 (-94.04%)
treecutFind nodes in hierarchical clustering that are statistically significant
Stars: ✭ 26 (-91.85%)
SealionThe first machine learning framework that encourages learning ML concepts instead of memorizing class functions.
Stars: ✭ 278 (-12.85%)
altairAssessing Source Code Semantic Similarity with Unsupervised Learning
Stars: ✭ 42 (-86.83%)
deep-INFOMAXChainer implementation of deep-INFOMAX
Stars: ✭ 32 (-89.97%)
BaySMMModel for learning document embeddings along with their uncertainties
Stars: ✭ 25 (-92.16%)
briefmatchBriefMatch real-time GPU optical flow
Stars: ✭ 36 (-88.71%)
dadsCode for 'Dynamics-Aware Unsupervised Discovery of Skills' (DADS). Enables skill discovery without supervision, which can be combined with model-based control.
Stars: ✭ 138 (-56.74%)
NMFADMMA sparsity aware implementation of "Alternating Direction Method of Multipliers for Non-Negative Matrix Factorization with the Beta-Divergence" (ICASSP 2014).
Stars: ✭ 39 (-87.77%)
SimclrPyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations by T. Chen et al.
Stars: ✭ 293 (-8.15%)
Ransac Flow(ECCV 2020) RANSAC-Flow: generic two-stage image alignment
Stars: ✭ 265 (-16.93%)
kwxBERT, LDA, and TFIDF based keyword extraction in Python
Stars: ✭ 33 (-89.66%)
DiscoveryMining Discourse Markers for Unsupervised Sentence Representation Learning
Stars: ✭ 48 (-84.95%)
Unsupervised-Learning-in-RWorkshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).
Stars: ✭ 34 (-89.34%)
adareg-monodispnetRepository for Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction (CVPR2019)
Stars: ✭ 22 (-93.1%)
Pytorch Vsumm ReinforceAAAI 2018 - Unsupervised video summarization with deep reinforcement learning (PyTorch)
Stars: ✭ 283 (-11.29%)
youtokentome-rubyHigh performance unsupervised text tokenization for Ruby
Stars: ✭ 17 (-94.67%)
dti-clustering(NeurIPS 2020 oral) Code for "Deep Transformation-Invariant Clustering" paper
Stars: ✭ 60 (-81.19%)
al-fk-self-supervisionOfficial PyTorch code for CVPR 2020 paper "Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision"
Stars: ✭ 28 (-91.22%)
FastmotHigh-performance multiple object tracking based on YOLO, Deep SORT, and optical flow
Stars: ✭ 284 (-10.97%)
flownet2-ColabGoogle Colab notebook for running Nvidia flownet2-pytorch
Stars: ✭ 23 (-92.79%)
ML2017FALLMachine Learning (EE 5184) in NTU
Stars: ✭ 66 (-79.31%)
SpeechsplitUnsupervised Speech Decomposition Via Triple Information Bottleneck
Stars: ✭ 266 (-16.61%)
KD3AHere is the official implementation of the model KD3A in paper "KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation".
Stars: ✭ 63 (-80.25%)
MVGLTCyb 2018: Graph learning for multiview clustering
Stars: ✭ 26 (-91.85%)
Indoor-SfMLearner[ECCV'20] Patch-match and Plane-regularization for Unsupervised Indoor Depth Estimation
Stars: ✭ 115 (-63.95%)
Chinese Ufldl Tutorial[UNMAINTAINED] 非监督特征学习与深度学习中文教程,该版本翻译自新版 UFLDL Tutorial 。建议新人们去学习斯坦福的CS231n课程,该门课程在网易云课堂上也有一个配有中文字幕的版本。
Stars: ✭ 303 (-5.02%)
srVAEVAE with RealNVP prior and Super-Resolution VAE in PyTorch. Code release for https://arxiv.org/abs/2006.05218.
Stars: ✭ 56 (-82.45%)
catgan pytorchUnsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
Stars: ✭ 50 (-84.33%)
DRNETPyTorch implementation of the NIPS 2017 paper - Unsupervised Learning of Disentangled Representations from Video
Stars: ✭ 45 (-85.89%)
CorexCorEx or "Correlation Explanation" discovers a hierarchy of informative latent factors. This reference implementation has been superseded by other versions below.
Stars: ✭ 266 (-16.61%)
PiCIEPiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in clustering (CVPR2021)
Stars: ✭ 102 (-68.03%)
PICParametric Instance Classification for Unsupervised Visual Feature Learning, NeurIPS 2020
Stars: ✭ 41 (-87.15%)
LinearCorexFast, linear version of CorEx for covariance estimation, dimensionality reduction, and subspace clustering with very under-sampled, high-dimensional data
Stars: ✭ 39 (-87.77%)
Deep-Association-LearningTensorflow Implementation on Paper [BMVC2018]Deep Association Learning for Unsupervised Video Person Re-identification
Stars: ✭ 68 (-78.68%)
L2cLearning to Cluster. A deep clustering strategy.
Stars: ✭ 262 (-17.87%)
pwcnetPWC-Network with TensorFlow
Stars: ✭ 72 (-77.43%)
SimCLR-in-TensorFlow-2(Minimally) implements SimCLR (https://arxiv.org/abs/2002.05709) in TensorFlow 2.
Stars: ✭ 75 (-76.49%)
Real-Time-Abnormal-Events-Detection-and-Tracking-in-Surveillance-SystemThe main abnormal behaviors that this project can detect are: Violence, covering camera, Choking, lying down, Running, Motion in restricted areas. It provides much flexibility by allowing users to choose the abnormal behaviors they want to be detected and keeps track of every abnormal event to be reviewed. We used three methods to detect abnorma…
Stars: ✭ 35 (-89.03%)