There are 0 repository under center-loss topic.
Pytorch implementation of Center Loss
A PyTorch implementation of center loss on MNIST
An unofficial Gluon FR Toolkit for face recognition. https://gluon-face.readthedocs.io
One-shot Learning and deep face recognition notebooks and workshop materials
center loss for face recognition
Deep Face Recognition in PyTorch
人脸识别算法,结合facenet网络结构和center loss作为损失,基于tensorflow框架,含训练和测试代码,支持从头训练和摄像头测试
Open Set Recognition
Deep Attentive Center Loss
This project is intended to solve the task of massive image retrieval.
Adversarial Background-Aware Loss for Weakly-supervised Temporal Activity Localization (ECCV 2020)
Simple Keras implementation of Triplet-Center Loss on the MNIST dataset
Face Recognition Project on Pytorch
PyTorch Implementation for the paper "C3VQG: Category Consistent Cyclic Visual Question Generation" (ACM MM Asia'20).
PyTorch Implementation for the paper "DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors" (ECCVW'20).
This is an implementation of the Center Loss article (2016).
Similarity Learning applied to Speaker Verification and Semantic Textual Similarity
keras implementation of A Discriminative Feature Learning Approach for Deep Face Recognition based on MNIST
This repository contains the ipynb for a project on deep learning visual classification of food categories
Official companion repository for the paper "A Metric Learning Approach to Misogyny Categorization" at the 5th Workshop on Representation Learning for NLP, ACL 2020
Evaluating the effectiveness of using standalone center loss.
The final project of DLCV course (CommE 5052) on NTU
Based on https://github.com/Arsey/keras-transfer-learning-for-oxford102, but more things are done in the project. Especially for the triplet and center loss.
training model using center-loss for face recognition
In this repository, we implement and review state of the art papers in the field of face recognition and face detection, and perform operations such as face verification and face identification with Deep models like Arcface, MTCNN, Facenet and so on.
One-shot face identification using deep learning
keras implementation of metric-based methods (center-loss, circle-loss, triplets...)
PyTorch implementation of "Open-set Recognition of Unseen Macromolecules in Cellular Electron Cryo-Tomograms by Soft Large Margin Centralized Cosine Loss"
Hybrid Data Augmentation and Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition
Basic conception of loss function, dimension reduction, transfer learning, image classification.