作者
Abhijit Kundu, Andrea Tagliasacchi, Anissa Yuenming Mak, Austin Stone, Carl Doersch, Cengiz Oztireli, Charles Herrmann, Dan Gnanapragasam, Daniel Duckworth, Daniel Rebain, David James Fleet, Deqing Sun, Derek Nowrouzezahrai, Dmitry Lagun, Etienne Pot, Fangcheng Zhong, Florian Golemo, Francois Belletti, Henning Meyer, Hsueh-Ti Derek Liu, Issam Laradji, Klaus Greff, Kwang Moo Yi, Lucas Beyer, Matan Sela, Mehdi SM Sajjadi, Noha Radwan, Sara Sabour, Suhani Vora, Thomas Kipf, Tianhao Wu, Vincent Sitzmann, Yilun Du, Yishu Miao
发表日期
2022
简介
Data is the driving force of machine learning. The amount and quality of training data is often more important for the performance of a system than the details of its architecture. Data is also an important tool for testing specific hypothesis, and for empirically evaluating the behaviour of complex systems. Synthetic data generation represents a powerful tool that can address all these shortcomings: 1) it is cheap 2) supports rich ground-truth annotations 3) offers full control over data and 4) can circumvent privacy and legal concerns. Unfortunately the toolchain for generating data is less well developed than that for building models. We aim to improve this situation by introducing Kubric: a scalable open-source pipeline for generating realistic image and video data with rich ground truth annotations. We also publish a collection of generated datasets and baseline results on several vision tasks.
学术搜索中的文章
A Kundu, A Tagliasacchi, AY Mak, A Stone, C Doersch… - 2022