Tracker

Tracing Versus Freehand for Evaluating Computer-Generated Drawings

Tracing Versus Freehand for Evaluating Computer-Generated Drawings (SIGGRAPH 2021) Zeyu Wang, Sherry Qiu, Nicole Feng, Holly Rushmeier, Leonard McMillan, Julie Dorsey Drawing Dataset The dataset consists of 1,498 tracings and freehand drawings by 110 participants for 100 image prompts. Our drawings are registered to the prompts and include vector-based timestamped strokes collected via stylus input. Please right click the links below and “Save link as…” if it doesn’t download automatically. Image prompts. All rendered tracings and freehand drawings in SVG […]

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Transformer

Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis

Daft-Exprt – PyTorch Implementation PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis The validation logs up to 70K of synthesized mel and alignment are shown below (VCTK_val_p237-088). DATASET refers to the names of datasets such as VCTK in the following documents. Dependencies You can install the Python dependencies with pip3 install -r requirements.txt Also, Dockerfile is provided for Docker users. Inference You have to download the pretrained models and put them in output/ckpt/DATASET/. For a […]

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Images

Mutual-Channel Loss for Fine-Grained Image Classification

Mutual-Channel-Loss Code release for The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification (TIP 2020)DOI Changelog 2020/09/14 update the code: CUB-200-2011_ResNet18.py Training with ResNet18 (TRAINED FROM SCRATCH). 2020/04/19 add the hyper-parameter fine-tune results. 2020/04/18 clean the code for better understanding. Dataset CUB-200-2011 Requirements python 3.6 PyTorch 1.2.0 torchvision Training Download datasets Train: python CUB-200-2011.py, the alpha and beta are the hyper-parameters of the MC-Loss Description : PyTorch CUB-200-2011 Training with VGG16 (TRAINED FROM SCRATCH). Hyper-parameter Loss = […]

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Models

A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling

SlotRefine A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling Reference Main paper to be cited (Di Wu et al., 2020) @article{wu2020slotrefine, title={Slotrefine: A fast non-autoregressive model for joint intent detection and slot filling}, author={Wu, Di and Ding, Liang and Lu, Fan and Xie, Jian}, booktitle={EMNLP}, year={2020} } Requirements Our system is build upon the THUMT codebase. tensorflow 1.12python 3.6 Usage sh train.atis.sh GitHub https://github.com/moore3930/SlotRefine    

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Neural Network

Feature Learning in Infinite-Width Neural Networks

Empirical Experiments in “Feature Learning in Infinite-width Neural Networks” This repo contains code to replicate our experiments (Word2Vec, MAML) in our paper Feature Learning in Infinite-Width Neural NetworksGreg Yang, Edward Hu In short, the code here will allow you to train feature learning infinite-width neural networks on Word2Vec and on Omniglot (via MAML). Our results on Word2Vec: Our Results on MAML: Please see the README in individual folders for more details. This is the 4th paper in the Tensor Programs […]

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Automation

Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera Systems

ACSC Automatic extrinsic calibration for non-repetitive scanning solid-state LiDAR and camera systems. System Architecture 1. Dependency Tested with Ubuntu 16.04 64-bit and Ubuntu 18.04 64-bit. ROS (tested with kinetic / melodic) Eigen 3.2.5 PCL 1.8 python 2.X / 3.X python-pcl opencv-python (>= 4.0) scipy scikit-learn transforms3d pyyaml mayavi (optional, for debug and visualization only) 2. Preparation 2.1 Download and installation Use the following commands to download this repo. Notice: the SUBMODULE should also be cloned. git clone –recurse-submodules https://github.com/HViktorTsoi/ACSC Compile […]

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Domain

Domain Consensus Clustering for Universal Domain Adaptation

Domain-Consensus-Clustering [CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation Prerequisites To install requirements: pip install -r requirements.txt Python 3.6 GPU Memory: 10GB Pytorch 1.4.0 Getting Started Download the dataset: Office-31, OfficeHome, VisDA, DomainNet. Data Folder structure: Your dataset DIR: |-Office/domain_adaptation_images | |-amazon | |-webcam | |-dslr |-OfficeHome | |-Art | |-Product | |-… |-VisDA | |-train | |-validataion |-DomainNet | |-clipart | |-painting | |-… You need you modify the data_path in config files, i.e., config.root Training Train on one […]

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Neural Machine Translation (NMT), NMT Leave a comment

Machine Translation Weekly 87: Notes from ACL 2021

The story of the science fiction novel Roadside Picnic by Arkady and Boris Strugatsky (mostly known via Tarkovsky’s 1979 film Stalker) takes place after an extraterrestrial event called the Visitation. Some aliens stopped by, made a roadside picnic, and left behind plenty of weird and dangerous objects having features that contemporary science cannot explain. Although the UN tries to prevent people from entering the visitation zones before everything gets fully explored and explained, objects from the zone are traded on […]

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Advanced, CHATBOT, Data Science, NLP, Programming, Project, Python, Unstructured Data

Build an end-end Currency Convertor chatbot with Python and Dialogflow

This article was published as a part of the Data Science Blogathon Introduction Hello all, Hope you are fine. In this tutorial we will learn how to create chatbots using Dialogflow and python, as well we will learn the deployment of chatbots to telegram. In our previous articles, we have learned to create a simple rule-based chatbot using simple python and NLTK libraries. I would like to request you to have a look at the article creating a simple chatbot […]

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