triplet-loss

Siamese and triplet networks with online pair/triplet mining in PyTorch

label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful

Implementation of triplet loss in TensorFlow

Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

Unsupervised Scalable Representation Learning for Multivariate Time Series: Experiments

Keras implementation of ‘’Deep Speaker: an End-to-End Neural Speaker Embedding System‘’ (speaker recognition)

Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification

Person re-ID baseline with triplet loss

A PyTorch implementation of the 'FaceNet' paper for training a facial recognition model with Triplet Loss using the glint360k dataset. A pre-trained model using Triplet Loss is available for download.

Complete Code for "Hard-Aware-Deeply-Cascaded-Embedding"

Re-implementation of tripletloss function in FaceNet

A PyTorch-based toolkit for natural language processing

A PyTorch implementation of CGD based on the paper "Combination of Multiple Global Descriptors for Image Retrieval"

Learning 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

A generic triplet data loader for image classification problems,and a triplet loss net demo.

Image similarity using Triplet Loss

One-Shot Learning with Triplet CNNs in Pytorch

Deep Learning - one shot learning for speaker recognition using Filter Banks

2020/2021 HKUST CSE FYP Masked Facial Recognition, developer: Sam Yuen, Alex Xie, Tony Cheng

Keras implementation of "Deep Learning using Triplet Network" by Hoffer and Ailon. https://arxiv.org/pdf/1412.6622.pdf

MassFace: an effecient implementation using triplet loss for face recognition

Using SigComp'11 dataset for signature verification

This project is intended to solve the task of massive image retrieval.

Highly efficient PyTorch version of the Semi-hard Triplet loss ⚡️

Adversarial Background-Aware Loss for Weakly-supervised Temporal Activity Localization (ECCV 2020)

Who is your doppelgänger and more with Keras face recognition

Deepfakes Video classification via CNN, LSTM, C3D and triplets

This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.