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"
An implementation of MobileNetV3 with pyTorch
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 smoothing PyTorch implementation
Source code of our paper "Focus on the Target’s Vocabulary: Masked Label Smoothing for Machine Translation" @acl-2022
Mean Teacher-based Cross-Domain Activity Recognition using WiFi Signals, IoTJ 2023
Supplementary material and code for "From Label Smoothing to Label Relaxation" as published at AAAI 2021.
Build an algorithm that can predict multiple future states of Limit Order Books using high-frequency, multi-variate, short time-frame data
Label smoothed Aggregation cross entropy loss for generalisation in sequence to sequence tasks.
Label Smoothing applied in Focal Loss
[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.
Noise Injection Techniques provides a comprehensive exploration of methods to make machine learning models more robust to real-world bad data. This repository explains and demonstrates Gaussian noise, dropout, mixup, masking, adversarial noise, and label smoothing, with intuitive explanations, theory, and practical code examples.
📦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
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