1,763 skills found · Page 3 of 59
SLDGroup / EMCADOfficial repository of CVPR 2024 paper "EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation"
rossgoodwin / NeuralsnapGenerates poetry from images using convolutional and recurrent neural networks
GitYCC / Crnn PytorchConvolutional Recurrent Neural Network (CRNN) for image-based sequence recognition using Pytorch
layumi / Image Text EmbeddingTOMM2020 Dual-Path Convolutional Image-Text Embedding with Instance Loss :feet: https://arxiv.org/abs/1711.05535
CQFIO / FastImageProcessingFast Image Processing with Fully-Convolutional Networks
martinkersner / Py Img Seg EvalEvaluation metrics for image segmentation inspired by paper Fully Convolutional Networks for Semantic Segmentation
huangzehao / Caffe VdsrA Caffe-based implementation of very deep convolution network for image super-resolution
jibikbam / CNN 3D Images Tensorflow3D image classification using CNN (Convolutional Neural Network)
danfenghong / IEEE TGRS GCNDanfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph Convolutional Networks for Hyperspectral Image Classification, IEEE Trans. Geosci. Remote Sens., 2021, 59(7): 5966-5978.
LabSAINT / SPD ConvCode for ECML PKDD 2022 paper: No More Strided Convolutions or Pooling: A Novel CNN Architecture for Low-Resolution Images and Small Objects
mitmul / Ssai CnnSemantic Segmentation for Aerial / Satellite Images with Convolutional Neural Networks including an unofficial implementation of Volodymyr Mnih's methods
Jongchan / Tensorflow VdsrA tensorflow implementation of "Accurate Image Super-Resolution Using Very Deep Convolutional Networks", CVPR 16'
treigerm / WaterNetA convolutional neural network that identifies water in satellite images.
zwx8981 / DBCNN PyTorchAn experimental Pytorch implementation of Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
imatge-upc / Retrieval 2017 CamClass-Weighted Convolutional Features for Image Retrieval (BMVC 2017)
ImagingLab / ICIAR2018Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification. ICIAR 2018 Grand Challenge on BreAst Cancer Histology images (BACH)
nipponjo / Deepfillv2 Pytorch🖼️🎨 A PyTorch reimplementation of the paper Free-Form Image Inpainting with Gated Convolution (DeepFill v2) (https://arxiv.org/abs/1806.03589)
ZFTurbo / ZF UNET 224 Pretrained ModelModification of convolutional neural net "UNET" for image segmentation in Keras framework
lmb-freiburg / Mv3dMulti-view 3D Models from Single Images with a Convolutional Network
piyushpathak03 / Recommendation SystemsRecommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems
