August 4, 2021 Machine Learning

Boosting Co-teaching with Compression Regularization for Label Noise

Nested-Co-teaching ([email protected]) Pytorch implementation of paper “Boosting Co-teaching with Compression Regularization for Label Noise” [PDF] If our project is helpful for your research, please consider citing : @inproceedings{chen2021boosting, title={Boosting Co-teaching with Compression Regularization for Label Noise}, author={Chen, Yingyi and Shen, Xi and Hu, Shell Xu and Suykens, Johan AK}, booktitle={CVPR Learning from Limited and Imperfect Data (L2ID) workshop}, year={2021} } Our model can be learnt in a single GPU GeForce GTX 1080Ti (12G), this code has been tested with Pytorch […]

August 4, 2021 Transformer

Image data augmentation scheduler for albumentations transforms

albu_scheduler Scheduler for albumentations transforms based on PyTorch schedulers interface TransformMultiStepScheduler import albumentations as A from albu_scheduler import TransformMultiStepScheduler transform_1 = A.Compose([ A.RandomCrop(width=256, height=256), A.HorizontalFlip(p=0.5), A.RandomBrightnessContrast(p=0.2), ]) transform_2 = A.Compose([ A.RandomCrop(width=128, height=128), A.VerticalFlip(p=0.5), ]) scheduled_transform = TransformMultiStepScheduler(transforms=[transform_1, transform_2], milestones=[0, 10]) dataset = Dataset(transform=scheduled_transform) for epoch in range(100): train(…) validate(…) scheduled_transform.step() TransformSchedulerOnPlateau from albu_scheduler import TransformSchedulerOnPlateau scheduled_transform = TransformSchedulerOnPlateau(transforms=[transform_1, transform_2], mode=”max”, patience=5) dataset = Dataset(transform=scheduled_transform) for epoch in range(100): train(…) score = validate(…) scheduled_transform.step(score) git clone https://github.com/KiriLev/albu_scheduler cd albu_scheduler make install […]

August 4, 2021 Beginner, NLP, Python, Text, Unstructured Data

Performing Sentiment Analysis Using Twitter Data!

Photo by Daddy Mohlala on Unsplash Data is water, purifying to make it edible is a role of Data Analyst – Kashish Rastogi We are going to clean the twitter text data and visualize data in this blog. Table Of Contents: Problem Statement Data Description Cleaning text with NLP Finding if the text has: with spacy Cleaning text with preprocessor library Analysis of the sentiment of data Data visualizing I am taking the twitter data which is available here on […]

August 4, 2021 Advanced, NLP, Project, Python, Structured Data, Text

Training BERT Text Classifier on Tensor Processing Unit (TPU)

Training hugging face most famous model on TPU for social media Tunisian Arabizi sentiment analysis. Introduction The Arabic speakers usually express themself in local dialect on social media, so Tunisians use Tunisian Arabizi which consists of Arabic written in form of Latin alphabets. The sentiment analysis relies on cultural knowledge and word sense with contextual information. We will be using both Arabizi dialect and sentimental analysis to solve the problem in this project. The competition is hosted on Zindi which […]

August 3, 2021 NLP

Dialogue in the Wild: Learning from a Deployed Role-Playing Game with Humans and Bots

Abstract Much of NLP research has focused on crowdsourced static datasets and the supervised learning paradigm of training once and then evaluating test performance. As argued in de Vries et al. (2020), crowdsourced data has the issues of lack of naturalness and relevance to real-world use cases, while the static dataset paradigm does not allow for a model to learn from its experiences of using language (Silver et al., 2013). In contrast, one might hope for machine learning systems that […]

August 3, 2021 PyTorch

UMS for Multi-turn Response Selection in PyTorch

UMS for Multi-turn Response Selection PyTorch Implementation for AAAI’21 “Do Response Selection Models Really Know What’s Next? Utterance Manipulation Strategies for Multi-turn Response Selection” Implements the model described in the following paper Do Response Selection Models Really Know What’s Next? Utterance Manipulation Strategies for Multi-turn Response Selection. @inproceedings{whang2021ums, title={Do Response Selection Models Really Know What’s Next? Utterance Manipulation Strategies for Multi-turn Response Selection}, author={Whang, Taesun and Lee, Dongyub and Oh, Dongsuk and Lee, Chanhee and Han, Kijong and Lee, Dong-hun […]

August 3, 2021 Field

Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds

This repository contains the PyTorch implementation for paper “PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds” (CVPR 2021)[arXiv] Installation Prerequisites Python 3.8 PyTorch 1.8 torch-scatter CUDA 10.2 RTX 2080 Ti tqdm, tensorboard, scipy, imageio, png conda create -n pvraft python=3.8 conda activate pvraft conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch conda install tqdm tensorboard scipy imageio pip install pypng pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cu102.html Usage Data Preparation We follow HPLFlowNet to prepare FlyingThings3D and KITTI datasets. […]

August 3, 2021 Text

Evaluating the Factual Consistency of Abstractive Text Summarization

factCC Evaluating the Factual Consistency of Abstractive Text SummarizationAuthors: Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher Introduction Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents.We propose a weakly-supervised, model-based approach for verifying factual consistency and identifying conflicts between source documents and a generated summary.Training data is generated by applying a series of rule-based transformations to the sentences of source documents.The factual consistency model is then trained jointly […]

August 3, 2021 Task

Multi-Task Framework for Cross-Lingual Abstractive Summarization

MCLAS Multi-Task Framework for Cross-Lingual Abstractive Summarization (MCLAS)The code for ACL2021 paper Cross-Lingual Abstractive Summarization with Limited Parallel Resources (Paper). Some codes are borrowed from PreSumm (https://github.com/nlpyang/PreSumm). Environments Python version: This code is in Python3.7 Package Requirements: torch==1.1.0, transformers, tensorboardX, multiprocess, pyrouge Needs few changes to be compatible with torch 1.4.0~1.8.0, mainly tensor type (bool) bugs. Data Preparation To improve training efficiency, we preprocessed concatenated dataset (with target “monolingual summary + [LSEP] + cross-lingual summary”) and normal dataset (with target […]

August 3, 2021 Images

A deeply supervised image fusion network for change detection

A-deeply-supervised-image-fusion-network-for-change-detection-in-remote-sensing-images A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images深度监督影像融合网络DSIFN用于高分辨率双时相遥感影像变化检测Official implement of the Paper：A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. If you find this work helps in your research, please consider citing: 论文《A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening images》的官方模型代码。如果该代码对你的研究有所帮助，烦请引用： Zhang, C., Yue, P., Tapete, D., Jiang, L., Shangguan, B., Huang, L., & Liu, G. (2020). […]

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