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

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

Knowledge Distillation: CVPR2020 Oral, Revisiting Knowledge Distillation via Label Smoothing Regularization

Corrupted labels and label smoothing

[ICML2022 Long Talk] Official Pytorch implementation of "To Smooth or Not? When Label Smoothing Meets Noisy Labels"

Code of our method MbLS (Margin-based Label Smoothing) for network calibration. To Appear at CVPR 2022. Paper : https://arxiv.org/abs/2111.15430

An implementation of MobileNetV3 with pyTorch

label smoothing PyTorch implementation

Source code of our paper "Focus on the Target’s Vocabulary: Masked Label Smoothing for Machine Translation" @acl-2022

Label smoothed Aggregation cross entropy loss for generalisation in sequence to sequence tasks.

Supplementary material and code for "From Label Smoothing to Label Relaxation" as published at AAAI 2021.

Label Smoothing applied in Focal Loss

Mean Teacher-based Cross-Domain Activity Recognition using WiFi Signals, IoTJ 2023

Build an algorithm that can predict multiple future states of Limit Order Books using high-frequency, multi-variate, short time-frame data

[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.

📦Simple Tool Box with Pytorch

Soft Target and Label Smoothing in Text Classification for Probability Calibration of Output Distributions.

Adding Image-context in the Label Smoothing process via Geodesic distance

Building High Performance Convolutional Neural Networks with TensorFlow

A simple template for classifying things