Label Smoothing and Adversarial Robustness
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Updated
Nov 4, 2020 - Jupyter Notebook
Label Smoothing and Adversarial Robustness
📦Simple Tool Box with Pytorch
Soft Target and Label Smoothing in Text Classification for Probability Calibration of Output Distributions.
Multiple Generation Based Knowledge Distillation: A Roadmap
Implementations of different loss-correction techniques to help deep models learn under class-conditional label noise.
A simple template for classifying things
Anime Face Generation using GANS and Label Smoothing.
deep-learning image classification resnet50
Code for "Memorization-Dilation: Modeling Neural Collapse under Noise" as published at ICLR 2023.
Modern Eager TensorFlow implementation of Attention Is All You Need
Label Smoothing applied in Focal Loss
Build an algorithm that can predict multiple future states of Limit Order Books using high-frequency, multi-variate, short time-frame data
Building High Performance Convolutional Neural Networks with TensorFlow
Adding Image-context in the Label Smoothing process via Geodesic distance
Label smoothed Aggregation cross entropy loss for generalisation in sequence to sequence tasks.
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.
Source code of our paper "Focus on the Target’s Vocabulary: Masked Label Smoothing for Machine Translation" @acl-2022
[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
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