Hybrid Data Augmentation and Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition
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Updated
May 28, 2023
Hybrid Data Augmentation and Attention-based Dilated Convolutional-Recurrent Neural Networks for Speech Emotion Recognition
keras implementation of triplet-loss and triple-center-loss
The final project of DLCV course (CommE 5052) on NTU
Evaluating the effectiveness of using standalone center loss.
PyTorch implementation of "Open-set Recognition of Unseen Macromolecules in Cellular Electron Cryo-Tomograms by Soft Large Margin Centralized Cosine Loss"
Official companion repository for the paper "A Metric Learning Approach to Misogyny Categorization" at the 5th Workshop on Representation Learning for NLP, ACL 2020
Basic conception of loss function, dimension reduction, transfer learning, image classification.
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
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.
keras implementation of metric-based methods (center-loss, circle-loss, triplets...)
PyTorch Implementation for the paper "DisCont: Self-Supervised Visual Attribute Disentanglement using Context Vectors" (ECCVW'20).
training model using center-loss for face recognition
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
PyTorch Implementation for the paper "C3VQG: Category Consistent Cyclic Visual Question Generation" (ACM MM Asia'20).
This is an implementation of the Center Loss article (2016).
Similarity Learning applied to Speaker Verification and Semantic Textual Similarity
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