679 Repositories
Python contrastive-feature-loss Libraries
I will implement Fastai in each projects present in this repository.
DEEP LEARNING FOR CODERS WITH FASTAI AND PYTORCH The repository contains a list of the projects which I have worked on while reading the book Deep Lea
This repository consists of a complete guide on natural language processing (NLP) in Python where we'll learn various techniques for implementing NLP including parsing & text processing and understand how to use NLP for text feature engineering.
Python_Natural_Language_Processing This repository contains tutorials on important topics related to Natural Language Processing (NPL). No. Name 01 01
A workshop with several modules to help learn Feast, an open-source feature store
Workshop: Learning Feast This workshop aims to teach users about Feast, an open-source feature store. We explain concepts & best practices by example,
[CVPR 2022 Oral] Crafting Better Contrastive Views for Siamese Representation Learning
Crafting Better Contrastive Views for Siamese Representation Learning (CVPR 2022 Oral) 2022-03-29: The paper was selected as a CVPR 2022 Oral paper! 2
Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch
CoCa - Pytorch Implementation of CoCa, Contrastive Captioners are Image-Text Foundation Models, in Pytorch. They were able to elegantly fit in contras
code for TCL: Vision-Language Pre-Training with Triple Contrastive Learning, CVPR 2022
Vision-Language Pre-Training with Triple Contrastive Learning, CVPR 2022 News (03/16/2022) upload retrieval checkpoints finetuned on COCO and Flickr T
SentimentArcs: a large ensemble of dozens of sentiment analysis models to analyze emotion in text over time
SentimentArcs - Emotion in Text An end-to-end pipeline based on Jupyter notebooks to detect, extract, process and anlayze emotion over time in text. E
Official implementation of "CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding" (CVPR, 2022)
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding (CVPR'22) Paper Link | Project Page Abstract : Manual an
A PyTorch implementation of ICLR 2022 Oral paper PiCO: Contrastive Label Disambiguation for Partial Label Learning
PiCO: Contrastive Label Disambiguation for Partial Label Learning This is a PyTorch implementation of ICLR 2022 Oral paper PiCO; also see our Project
🧬 Non-linear feature reduction using Deep Autoencoders and Breast Cancer classification.
Project summary This repository contains the implementation of my bachelor degree project. The aim of the project is to apply non-linear feature reduc
WarpRNNT loss ported in Numba CPU/CUDA for Pytorch
RNNT loss in Pytorch - Numba JIT compiled (warprnnt_numba) Warp RNN Transducer Loss for ASR in Pytorch, ported from HawkAaron/warp-transducer and a re
Code for Max-Margin Contrastive Learning - AAAI 2022
Max-Margin Contrastive Learning This is a pytorch implementation for the paper Max-Margin Contrastive Learning accepted to AAAI 2022. This repository
CLIP (Contrastive Language–Image Pre-training) for Italian
Italian CLIP CLIP (Radford et al., 2021) is a multimodal model that can learn to represent images and text jointly in the same space. In this project,
PyGCL: A PyTorch Library for Graph Contrastive Learning
PyGCL is a PyTorch-based open-source Graph Contrastive Learning (GCL) library, which features modularized GCL components from published papers, standa
Ego4d dataset repository. Download the dataset, visualize, extract features & example usage of the dataset
Ego4D EGO4D is the world's largest egocentric (first person) video ML dataset and benchmark suite, with 3,600 hrs (and counting) of densely narrated v
Implementation of the famous Image Manipulation\Forgery Detector "ManTraNet" in Pytorch
Who has never met a forged picture on the web ? No one ! Everyday we are constantly facing fake pictures touched up in Photoshop but it is not always
Official implementation for "QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation" (CVPR 2022)
QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation (CVPR2022) https://arxiv.org/abs/2203.08483 Unpaired image-to-image (I2I
Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation (CVPR 2022)
CCAM (Unsupervised) Code repository for our paper "CCAM: Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localizati
Implementation of some unbalanced loss like focal_loss, dice_loss, DSC Loss, GHM Loss et.al
Implementation of some unbalanced loss for NLP task like focal_loss, dice_loss, DSC Loss, GHM Loss et.al Summary Here is a loss implementation reposit
[CVPR 2022 Oral] Rethinking Minimal Sufficient Representation in Contrastive Learning
Rethinking Minimal Sufficient Representation in Contrastive Learning PyTorch implementation of Rethinking Minimal Sufficient Representation in Contras
NAACL 2022: MCSE: Multimodal Contrastive Learning of Sentence Embeddings
MCSE: Multimodal Contrastive Learning of Sentence Embeddings This repository contains code and pre-trained models for our NAACL-2022 paper MCSE: Multi
ProtFeat is protein feature extraction tool that utilizes POSSUM and iFeature.
Description: ProtFeat is designed to extract the protein features by employing POSSUM and iFeature python-based tools. ProtFeat includes a total of 39
SCALoss: Side and Corner Aligned Loss for Bounding Box Regression (AAAI2022).
SCALoss PyTorch implementation of the paper "SCALoss: Side and Corner Aligned Loss for Bounding Box Regression" (AAAI 2022). Introduction IoU-based lo
In this project we predict the forest cover type using the cartographic variables in the training/test datasets.
Kaggle Competition: Forest Cover Type Prediction In this project we predict the forest cover type (the predominant kind of tree cover) using the carto
[CVPR 2022] Official code for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration"
MDCA Calibration This is the official PyTorch implementation for the paper: "A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved
Arcpy Tool developed for ArcMap 10.x that checks DVOF points against TDS data and creates an output feature class as well as a check database.
DVOF_check_tool Arcpy Tool developed for ArcMap 10.x that checks DVOF points against TDS data and creates an output feature class as well as a check d
[CVPR 2022] Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Using Unreliable Pseudo Labels Official PyTorch implementation of Semi-Supervised Semantic Segmentation Using Unreliable Pseudo Labels, CVPR 2022. Ple
ReLoss - Official implementation for paper "Relational Surrogate Loss Learning" ICLR 2022
Relational Surrogate Loss Learning (ReLoss) Official implementation for paper "R
Diabetes-Feature-Engineering - A machine learning model that can predict whether people have diabetes when their characteristics are specified
Diabetes-Feature-Engineering Aim Developing a machine learning model that can pr
SimCTG - A Contrastive Framework for Neural Text Generation
A Contrastive Framework for Neural Text Generation Authors: Yixuan Su, Tian Lan,
SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING - The Facebook paper about fine tuning RoBERTa with contrastive loss
"# SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING" i
LotteryBuyPredictionWebApp - Lottery Purchase Prediction Model
Lottery Purchase Prediction Model Objective and Goal Predict the lottery type th
Code and Datasets from the paper "Self-supervised contrastive learning for volcanic unrest detection from InSAR data"
Code and Datasets from the paper "Self-supervised contrastive learning for volcanic unrest detection from InSAR data" You can download the pretrained
HCQ: Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval
HCQ: Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval [toc] 1. Introduction This repository provides the code for our paper at
PyTorch implementation of the Crafting Better Contrastive Views for Siamese Representation Learning
Crafting Better Contrastive Views for Siamese Representation Learning This is the official PyTorch implementation of the ContrastiveCrop paper: @artic
Dataset Condensation with Contrastive Signals
Dataset Condensation with Contrastive Signals This repository is the official implementation of Dataset Condensation with Contrastive Signals (DCC). T
CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensors
CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensors In order to facilitate the res
Speech Recognition is an important feature in several applications used such as home automation, artificial intelligence
Speech Recognition is an important feature in several applications used such as home automation, artificial intelligence, etc. This article aims to provide an introduction on how to make use of the SpeechRecognition and pyttsx3 library of Python.
Using Opencv ,based on Augmental Reality(AR) and will show the feature matching of image and then by finding its matching
Using Opencv ,this project is based on Augmental Reality(AR) and will show the feature matching of image and then by finding its matching ,it will just mask that image . This project ,if used in cctv then it will detect black listed people if mentioned properly with their images.
PyTorch-lightning implementation of the ESFW module proposed in our paper Edge-Selective Feature Weaving for Point Cloud Matching
Edge-Selective Feature Weaving for Point Cloud Matching This repository contains a PyTorch-lightning implementation of the ESFW module proposed in our
Official implementation for paper: Feature-Style Encoder for Style-Based GAN Inversion
Feature-Style Encoder for Style-Based GAN Inversion Official implementation for paper: Feature-Style Encoder for Style-Based GAN Inversion. Code will
PyTorch implementation for NCL (Neighborhood-enrighed Contrastive Learning)
NCL (Neighborhood-enrighed Contrastive Learning) This is the official PyTorch implementation for the paper: Zihan Lin*, Changxin Tian*, Yupeng Hou* Wa
A Broad Study on the Transferability of Visual Representations with Contrastive Learning
A Broad Study on the Transferability of Visual Representations with Contrastive Learning This repository contains code for the paper: A Broad Study on
A python scripts that uses 3 different feature extraction methods such as SIFT, SURF and ORB to find a book in a video clip and project trailer of a movie based on that book, on to it.
A python scripts that uses 3 different feature extraction methods such as SIFT, SURF and ORB to find a book in a video clip and project trailer of a movie based on that book, on to it.
Supervised Contrastive Learning for Product Matching
Contrastive Product Matching This repository contains the code and data download links to reproduce the experiments of the paper "Supervised Contrasti
Pytorch implementation of "Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates"
Peer Loss functions This repository is the (Multi-Class & Deep Learning) Pytorch implementation of "Peer Loss Functions: Learning from Noisy Labels wi
Nested cross-validation is necessary to avoid biased model performance in embedded feature selection in high-dimensional data with tiny sample sizes
Pruner for nested cross-validation - Sphinx-Doc Nested cross-validation is necessary to avoid biased model performance in embedded feature selection i
This repository provides a PyTorch implementation and model weights for HCSC (Hierarchical Contrastive Selective Coding)
HCSC: Hierarchical Contrastive Selective Coding This repository provides a PyTorch implementation and model weights for HCSC (Hierarchical Contrastive
Pytorch implementation of TailCalibX : Feature Generation for Long-tail Classification
TailCalibX : Feature Generation for Long-tail Classification by Rahul Vigneswaran, Marc T. Law, Vineeth N. Balasubramanian, Makarand Tapaswi [arXiv] [
ElasticFace: Elastic Margin Loss for Deep Face Recognition
This is the official repository of the paper: ElasticFace: Elastic Margin Loss for Deep Face Recognition Paper on arxiv: arxiv Model Log file Pretrain
This repository contains the implementation of the paper Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans
Contrastive Instance Association for 4D Panoptic Segmentation using Sequences of 3D LiDAR Scans This repository contains the implementation of the pap
Speech Emotion Recognition with Fusion of Acoustic- and Linguistic-Feature-Based Decisions
APSIPA-SER-with-A-and-T This code is the implementation of Speech Emotion Recognition (SER) with acoustic and linguistic features. The network model i
A pure PyTorch implementation of the loss described in "Online Segment to Segment Neural Transduction"
ssnt-loss ℹ️ This is a WIP project. the implementation is still being tested. A pure PyTorch implementation of the loss described in "Online Segment t
A PyTorch-based R-YOLOv4 implementation which combines YOLOv4 model and loss function from R3Det for arbitrary oriented object detection.
R-YOLOv4 This is a PyTorch-based R-YOLOv4 implementation which combines YOLOv4 model and loss function from R3Det for arbitrary oriented object detect
RuCLIP tiny (Russian Contrastive Language–Image Pretraining) is a neural network trained to work with different pairs (images, texts).
RuCLIPtiny Zero-shot image classification model for Russian language RuCLIP tiny (Russian Contrastive Language–Image Pretraining) is a neural network
Evaluate on three different ML model for feature selection using Breast cancer data.
Anomaly-detection-Feature-Selection Evaluate on three different ML model for feature selection using Breast cancer data. ML models: SVM, KNN and MLP.
PyTorch implementation of DirectCLR from paper Understanding Dimensional Collapse in Contrastive Self-supervised Learning
DirectCLR DirectCLR is a simple contrastive learning model for visual representation learning. It does not require a trainable projector as SimCLR. It
CLNTM - Contrastive Learning for Neural Topic Model
Contrastive Learning for Neural Topic Model This repository contains the impleme
Code for You Only Cut Once: Boosting Data Augmentation with a Single Cut
You Only Cut Once (YOCO) YOCO is a simple method/strategy of performing augmenta
Supervised Sliding Window Smoothing Loss Function Based on MS-TCN for Video Segmentation
SSWS-loss_function_based_on_MS-TCN Supervised Sliding Window Smoothing Loss Function Based on MS-TCN for Video Segmentation Supervised Sliding Window
DFFNet: An IoT-perceptive Dual Feature Fusion Network for General Real-time Semantic Segmentation
DFFNet Paper DFFNet: An IoT-perceptive Dual Feature Fusion Network for General Real-time Semantic Segmentation. Xiangyan Tang, Wenxuan Tu, Keqiu Li, J
RuCLIP-SB (Russian Contrastive Language–Image Pretraining SWIN-BERT) is a multimodal model for obtaining images and text similarities and rearranging captions and pictures. Unlike other versions of the model we use BERT for text encoder and SWIN transformer for image encoder.
ruCLIP-SB RuCLIP-SB (Russian Contrastive Language–Image Pretraining SWIN-BERT) is a multimodal model for obtaining images and text similarities and re
[ECE NTUA] 👁 Computer Vision - Lab Projects & Theoretical Problem Sets (2020-2021)
Computer Vision - NTUA (2020-2021) This repository hosts the lab projects and theoretical problem sets of the Computer Vision course held by ECE NTUA
Create N Share is a No Code solution which gives users the ability to create any type of feature rich survey forms with ease.
create n share Note : The Project Scaffold will be pushed soon. Create N Share is a No Code solution which gives users the ability to create any type
SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples
SNCSE SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples This is the repository for SNCSE. SNCSE aims to allev
Self-supervised learning algorithms provide a way to train Deep Neural Networks in an unsupervised way using contrastive losses
Self-supervised learning Self-supervised learning algorithms provide a way to train Deep Neural Networks in an unsupervised way using contrastive loss
This repository contains code to run experiments in the paper "Signal Strength and Noise Drive Feature Preference in CNN Image Classifiers."
Signal Strength and Noise Drive Feature Preference in CNN Image Classifiers This repository contains code to run experiments in the paper "Signal Stre
Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation
CorrNet This project provides the code and results for 'Lightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation'
Contrastive Loss Gradient Attack (CLGA)
Contrastive Loss Gradient Attack (CLGA) Official implementation of Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation, WWW22 Bu
A PyTorch implementation for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation".
Dual-Contrastive-Learning A PyTorch implementation for our paper "Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation". Y
PyTorch implementation of ICLR 2022 paper PiCO: Contrastive Label Disambiguation for Partial Label Learning
PiCO: Contrastive Label Disambiguation for Partial Label Learning This is a PyTorch implementation of ICLR 2022 paper PiCO: Contrastive Label Disambig
This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR)
CEDR This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR) introduced in the following paper
Hyperparameters tuning and features selection are two common steps in every machine learning pipeline.
shap-hypetune A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models. Overview Hyperparameters t
Detailed analysis on fraud claims in insurance companies, gives you information as to why huge loss take place in insurance companies
Insurance-Fraud-Claims Detailed analysis on fraud claims in insurance companies, gives you information as to why huge loss take place in insurance com
Code for our paper A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization,
FSRA This repository contains the dataset link and the code for our paper A Transformer-Based Feature Segmentation and Region Alignment Method For UAV
This repository contains pre-trained models and some evaluation code for our paper Towards Unsupervised Dense Information Retrieval with Contrastive Learning
Contriever: Towards Unsupervised Dense Information Retrieval with Contrastive Learning This repository contains pre-trained models and some evaluation
Unsupervised Feature Loss (UFLoss) for High Fidelity Deep learning (DL)-based reconstruction
Unsupervised Feature Loss (UFLoss) for High Fidelity Deep learning (DL)-based reconstruction Official github repository for the paper High Fidelity De
FocusFace: Multi-task Contrastive Learning for Masked Face Recognition
FocusFace This is the official repository of "FocusFace: Multi-task Contrastive Learning for Masked Face Recognition" accepted at IEEE International C
CRLT: A Unified Contrastive Learning Toolkit for Unsupervised Text Representation Learning
CRLT: A Unified Contrastive Learning Toolkit for Unsupervised Text Representation Learning This repository contains the code and relevant instructions
CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs
CLIP [Blog] [Paper] [Model Card] [Colab] CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pair
Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification
Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification
Kaggle DSTL Satellite Imagery Feature Detection
Kaggle DSTL Satellite Imagery Feature Detection
Generic ecosystem for feature extraction from aerial and satellite imagery
Note: Robosat is neither maintained not actively developed any longer by Mapbox. See this issue. The main developers (@daniel-j-h, @bkowshik) are no l
Keras Image Embeddings using Contrastive Loss
Image to Embedding projection in vector space. Implementation in keras and tensorflow of batch all triplet loss for one-shot/few-shot learning.
Grank is a feature-rich script that automatically grinds Dank Memer for you
Grank Inspired by this repository. This is a WIP and there will be more functions added in the future. What is Grank? Grank is a feature-rich script t
An easy-to-use feature store
A feature store is a data storage system for data science and machine-learning. It can store raw data and also transformed features, which can be fed straight into an ML model or training script.
A fairly common feature in web applications to have links that open a popover when hovered
Add Popovers to Links in Flask App It is a fairly common feature in web applications to have links that open a popover when hovered. Twitter does this
HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images
HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images Histological Image Segmentation This
Keras Image Embeddings using Contrastive Loss
Keras-Image-Embeddings-using-Contrastive-Loss Image to Embedding projection in vector space. Implementation in keras and tensorflow for custom data. B
Feature engineering and machine learning: together at last
Feature engineering and machine learning: together at last! Lambdo is a workflow engine which significantly simplifies data analysis by unifying featu
This is an open solution to the Home Credit Default Risk challenge 🏡
Home Credit Default Risk: Open Solution This is an open solution to the Home Credit Default Risk challenge 🏡 . More competitions 🎇 Check collection
Estimation of whether or not the persons given information will have diabetes.
Diabetes Business Problem : It is desired to develop a machine learning model that can predict whether people have diabetes when their characteristics
The goal of the exercises below is to evaluate the candidate knowledge and problem solving expertise regarding the main development focuses for the iFood ML Platform team: MLOps and Feature Store development.
The goal of the exercises below is to evaluate the candidate knowledge and problem solving expertise regarding the main development focuses for the iFood ML Platform team: MLOps and Feature Store development.
Codes and scripts for "Explainable Semantic Space by Grounding Languageto Vision with Cross-Modal Contrastive Learning"
Visually Grounded Bert Language Model This repository is the official implementation of Explainable Semantic Space by Grounding Language to Vision wit
Official Code Release for "CLIP-Adapter: Better Vision-Language Models with Feature Adapters"
Official Code Release for "CLIP-Adapter: Better Vision-Language Models with Feature Adapters" Pipeline of CLIP-Adapter CLIP-Adapter is a drop-in modul
CAMoE + Dual SoftMax Loss (DSL): Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss
CAMoE + Dual SoftMax Loss (DSL): Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss This is official implement of "
Python Computer Vision from Scratch
This repository explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.
CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Selection
CIFS This repository provides codes for CIFS (ICML 2021). CIFS: Improving Adversarial Robustness of CNNs via Channel-wise Importance-based Feature Sel
Implement of "Training deep neural networks via direct loss minimization" in PyTorch for 0-1 loss
This is the implementation of "Training deep neural networks via direct loss minimization" published at ICML 2016 in PyTorch. The implementation targe
📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.
📚 A collection of all the Deep Learning Metrics that I came across which are not accuracy/loss.