August 13, 2021 Machine Learning

Large scale embeddings on a single machine

Marius is a system under active development for training embeddings for large-scale graphs on a single machine. Training on large scale graphs requires a large amount of data movement to get embedding parameters from storage to the computational device.Marius is designed to mitigate/reduce data movement overheads using: Pipelined training and IO Partition caching and buffer-aware data orderings Details on how Marius works can be found in our OSDI ’21 Paper, where experiment scripts and configurations can be found in the […]

August 13, 2021 Machine Learning

Benchmarking Model and System Performance of Federated Learning

FedScale This repository contains scripts and instructions of building FedScale, a diverse set of challenging and realistic benchmark datasets to facilitate scalable, comprehensive, and reproducible federated learning (FL) research. FedScale datasets are large-scale, encompassing a diverse range of important FL tasks, such as image classification, object detection, language modeling, speech recognition, and reinforcement learning. For each dataset, we provide a unified evaluation protocol using realistic data splits and evaluation metrics. To meet the pressing need for reproducing realistic FL at […]

August 13, 2021 Images

SofGAN: A Portrait Image Generator with Dynamic Styling

This repository contains the official PyTorch implementation for the paper: SofGAN: A Portrait Image Generator with Dynamic Styling. We propose a SofGAN image generator to decouple the latent space of portraits into two subspaces: a geometry space and a texture space. Experiments on SofGAN show that our system can generate high quality portrait images with independently controllable geometry and texture attributes. Installation Install environment: git clone https://github.com/apchenstu/sofgan.git –recursive conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.2 -c pytorch pip install tqdm argparse scikit-image […]

August 13, 2021 Color

Paster color with blender color format in python

ColorPaster [Blender Addon] Convert three types of color in your clipboard and paste it to the color property (gamma correct) How to Use Hover your mouse on the color property,F3 search color paste, then bind it to short cut or quick favorite. Copy your color from your favorite picker or website, then paste it with your quick favorite Format Support Hex : ‘#’ is not necessary RGB :uppercase letter & space after ‘,’ is not necessary. Allow to pick RGBA […]

August 13, 2021 Transformer

A Transformer Model for Embodied, Language-guided Visual Task Completion

EmBERT We present Embodied BERT (EmBERT), a transformer-based model which can attend to high-dimensional, multi-modal inputs across long temporal horizons for language-conditioned task completion. Additionally, we bridge the gap between successful object-centric navigation models used for non-interactive agents and the language-guided visual task completion benchmark, ALFRED, by introducing object navigation targets for EmBERT training. We achieve competitive performance on the ALFRED benchmark, and EmBERT marks the first transformer-based model to successfully handle the long-horizon, dense, multi-modal histories of ALFRED, and […]

August 13, 2021 3D

Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo

Multi-View Multi-Person 3D Pose Estimation with Plane Sweep Stereo This repository includes the source code for our CVPR 2021 paper on multi-view multi-person 3D pose estimation. Please read our paper for more details at https://arxiv.org/abs/2104.02273. The project webpage is available here. Bibtex: @InProceedings{Lin_2021_CVPR, author = {Lin, Jiahao and Lee, Gim Hee}, title = {Multi-View Multi-Person 3D Pose Estimation With Plane Sweep Stereo}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year […]

August 13, 2021 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 […]

August 13, 2021 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 […]

August 13, 2021 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 = […]

August 13, 2021 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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