148 skills found · Page 1 of 5
xmuSistone / DragRankSquareedit personal information which enables users to drag and rank image order
wvangansbeke / Sparse Depth CompletionPredict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. (Ranked 1st place on KITTI) [MVA 2019]
akarshzingade / Image Similarity Deep RankingNo description available
jau123 / Nanobanana Trending Prompts1,300+ curated trending AI image prompts from X/Twitter, ranked by engagement. Works with NanoBanana Pro, GPT Image, Midjourney
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
Junjue-Wang / Rank1 Ali Tianchi Real World Image Forgery Localization Challenge2022阿里天池真实场景篡改图像检测挑战赛-冠军方案（1/1149）
Zhenye-Na / Image Similarity Using Deep Ranking🖼️ 𝖀𝖓𝖔𝖋𝖋𝖎𝖈𝖎𝖆𝖑 PyTorch implementation of "Learning Fine-grained Image Similarity with Deep Ranking" (arXiv:1404.4661)
WangWenhao0716 / ISC Track1 Submission[NeurIPS Challenge Rank 1st] The codes and related files to reproduce the results for Image Similarity Challenge Track 1.
liuyang12 / DeSCIRank Minimization for Snapshot Compressive Imaging (TPAMI'19)
WangWenhao0716 / ISC Track2 Submission[NeurIPS Challenge Rank 3rd] The codes and related files to reproduce the results for Image Similarity Challenge Track 2.
ChenDarYen / Key Locked Rank One Editing For Text To Image PersonalizationAn Pytorch implementation of the paper Key-Locked Rank One Editing for Text-to-Image Personalization
shallowdream204 / LoRA IR[arXiv 2024] LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image Restoration
SathwikTejaswi / Deep RankingLearning Fine-grained Image Similarity with Deep Ranking is a novel application of neural networks, where the authors use a new multi scale architecture combined with a triplet loss to create a neural network that is able to perform image search. This repository is a simplified implementation of the same
QuentinWach / Image RankerRank images using TrueSkill by comparing them against each other in the browser. 🖼📊
hli1221 / Imagefusion Infrared Visible Latlrrinfrared anf visible image fusion using latent low-rank representation
NVlabs / LoRWeBWe propose a novel modular framework that learns to dynamically mix low-rank adapters (LoRAs) to improve visual analogy learning, enabling flexible and generalizable image edits based on example transformations.
wenqihuang / LS Net Dynamic MRIThis the code repository for paper "Deep Low-rank plus Sparse Network for Dynamic MR Imaging".
bes-dev / Pytorch Clip BboxPytorch based library to rank predicted bounding boxes using text/image user's prompts.
scalad / MathML2MathTypeEquation使用C#调用MathType将MathML格式的公式转换为MathType类型的公式并写入到Word中)Using MathType to converting MathML to mathtype equation and embedded in microsoft word document. Also, you can convert html into word,including tables、image or rank tag
axiqia / Anomaly Detection In Hyperspectral Images Based On Low Rank And Sparse RepresentationNo description available
