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)
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.
Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification
Person re-ID baseline with triplet loss
A generic triplet data loader for image classification problems,and a triplet loss net demo.
2020/2021 HKUST CSE FYP Masked Facial Recognition, developer: Sam Yuen, Alex Xie, Tony Cheng
A PyTorch implementation of CGD based on the paper "Combination of Multiple Global Descriptors for Image Retrieval"
A PyTorch-based toolkit for natural language processing
Complete Code for "Hard-Aware-Deeply-Cascaded-Embedding"
Re-implementation of tripletloss function in FaceNet
Deep Learning - one shot learning for speaker recognition using Filter Banks
Image similarity using Triplet Loss
Highly efficient PyTorch version of the Semi-hard Triplet loss
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
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