This repository contains the official pytorch implementation for the paper “SAS: Self-Augmentation Strategy for Language Model Pre-training” based on Huggingface transformers version 4.3.0. Only the SAS without the disentangled attention mechanism is released for now. To be updated. File structure train.py: The file for pre-training. run_glue.py: The file for finetuning. models modeling_sas.py: The main algorithm for the SAS. trainer_sas.py: It is inherited from Huggingface transformers. It is mainly modified for data processing. utils: It includes all the utilities. data_collator_sas.py: It […]

Augmentation for Single-Image-Super-Resolution Implimentation CutBlur Cutout CutMix Cutup CutMixup Blend RGBPermutation Identity OneOf License cf. @solafune(https://solafune.com) コンテストの参加以外を目的とした利用及び商用利用は禁止されています。商用利用・その他当コンテスト以外で利用したい場合はお問い合わせください。() cf. @solafune(https://solafune.com) Use for any purpose other than participation in the competition or commercial use is prohibited. If you would like to use them for any of the above purposes, please contact us. GitHub View Github    

CleanX is an open source python library for exploring, cleaning and augmenting large datasets of X-rays, or certain other types of radiological images. JPEG files can be extracted from DICOM files or used directly. CleanX is an open source  python library for exploring, cleaning and augmenting large datasets of X-rays, or certain other types of radiological images.JPEG files can be extracted from DICOM files or used directly. The latest official release: primary author: Candace Makeda H. Moore other authors + […]

Introduction In real-world applications of machine learning, reliable and safe systems must considermeasures of performance beyond standard test set accuracy. These other goalsinclude out-of-distribution (OOD) robustness, prediction consistency, resilience toadversaries, calibrated uncertainty estimates, and the ability to detect anomalousinputs. However, improving performance towards these goals is often a balancingact that today’s methods cannot achieve without sacrificing performance on othersafety axes. For instance, adversarial training improves adversarial robustnessbut sharply degrades other classifier performance metrics. Similarly, strong dataaugmentation and regularization techniques often […]

This repository provides the official PyTorch implementation of the ICRA 2021 paper: Adversarial Differentiable Data Augmentation for Autonomous SystemsAuthor: Manli Shu, Yu Shen, Ming C Lin, Tom Goldstein Environment The code has been tested on: python == 3.7.9 pytorch == 1.10.0 torchvision == 0.8.2 kornia == 0.6.2More dependencies can be found at ./requirements.txt Hardware requirements: The default training and testing setting requires 1 GPU. Data Datasets appeared in our paper can be downloaded/generated by following the directions in this page. […]

Code for our paper GPT3Mix and conducting classification experiments using GPT-3 prompt-based data augmentation. Getting Started Installing Packages The main depedencies can be installed via pip install -r requirements.txt. Usage The main code is run through main.py. Check out –help for full list of commands. The code will automatically use the first GPU device, if detected. A typical command to run BERT-base 10 times on the 1% subsample set of the SST-2 dataset and computing the averageof all run is […]

MixRNet(Using mixup as regularization and tuning hyper-parameters for ResNets) Using mixup data augmentation as reguliraztion and tuning the hyper parameters of ResNet 50 models to achieve 94.57% test accuracy on CIFAR-10 Dataset. Link to paper network error % resnet-50 6.97 resnet-110 6.61 resnet-164 5.93 resnet-1001 7.61 This method 5.43 Overview Change the wandb api key to valid api key. Python 3.8 and pytorch 1.9 (works on older versions as well) main.py is to train model sweep.py and sweep_config.py are for […]

facilitates implementing deep neural-network backbones, data augmentations, optimizers and learning schedulers. backbones loss functions augumentation styles optimizers schedulers data types visualizations Refer to docs/installation.md for installion of general_backbone package. Model backone Currently, general_backbone supports more than 70 type of resnet models such as: resnet18, resnet34, resnet50, resnet101, resnet152, resnext50. All models is supported can be found in general_backbone.list_models() function: import general_backbone general_backbone.list_models() Results {‘resnet’: [‘resnet18’, ‘resnet18d’, ‘resnet34’, ‘resnet34d’, ‘resnet26’, ‘resnet26d’, ‘resnet26t’, ‘resnet50’, ‘resnet50d’, ‘resnet50t’, ‘resnet101’, ‘resnet101d’, ‘resnet152’, ‘resnet152d’, ‘resnet200’, ‘resnet200d’, […]