Adding Image-context in the Label Smoothing process via Geodesic distance
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Updated
Feb 27, 2024 - Python
Adding Image-context in the Label Smoothing process via Geodesic distance
Code for "Memorization-Dilation: Modeling Neural Collapse under Noise" as published at ICLR 2023.
Build an algorithm that can predict multiple future states of Limit Order Books using high-frequency, multi-variate, short time-frame data
Mean Teacher-based Cross-Domain Activity Recognition using WiFi Signals, IoTJ 2023
Modern Eager TensorFlow implementation of Attention Is All You Need
Anime Face Generation using GANS and Label Smoothing.
Supplementary material and code for "From Label Smoothing to Label Relaxation" as published at AAAI 2021.
Knowledge Distillation: CVPR2020 Oral, Revisiting Knowledge Distillation via Label Smoothing Regularization
Building High Performance Convolutional Neural Networks with TensorFlow
利用pytorch实现图像分类的一个完整的代码,训练,预测,TTA,模型融合,模型部署,cnn提取特征,svm或者随机森林等进行分类,模型蒸馏,一个完整的代码
[ICML 2022] This work investigates the compatibility between label smoothing (LS) and knowledge distillation (KD). We suggest to use an LS-trained teacher with a low-temperature transfer to render high performance students.
Code of our method MbLS (Margin-based Label Smoothing) for network calibration. To Appear at CVPR 2022. Paper : https://arxiv.org/abs/2111.15430
label-smooth, amsoftmax, partial-fc, focal-loss, triplet-loss, lovasz-softmax. Maybe useful
[ICML2022 Long Talk] Official Pytorch implementation of "To Smooth or Not? When Label Smoothing Meets Noisy Labels"
A simple template for classifying things
Source code of our paper "Focus on the Target’s Vocabulary: Masked Label Smoothing for Machine Translation" @acl-2022
Multiple Generation Based Knowledge Distillation: A Roadmap
deep-learning image classification resnet50
Implementations of different loss-correction techniques to help deep models learn under class-conditional label noise.
📦Simple Tool Box with Pytorch
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