PyTorch Adapt

Research output: Working paper › Preprint › Research

Standard

PyTorch Adapt. / Musgrave, Kevin; Belongie, Serge; Lim, Ser-Nam.

arXiv.org, 2022.

Research output: Working paper › Preprint › Research

Harvard

Musgrave, K, Belongie, S & Lim, S-N 2022 'PyTorch Adapt' arXiv.org. <https://arxiv.org/abs/2211.15673>

APA

Musgrave, K., Belongie, S., & Lim, S-N. (2022). PyTorch Adapt. arXiv.org. https://arxiv.org/abs/2211.15673

Vancouver

Musgrave K, Belongie S, Lim S-N. PyTorch Adapt. arXiv.org. 2022.

Author

Musgrave, Kevin ; Belongie, Serge ; Lim, Ser-Nam. / PyTorch Adapt. arXiv.org, 2022.

Bibtex

@techreport{4c952468609441bc92f855a2276e01d0,
title = "PyTorch Adapt",
abstract = "PyTorch Adapt is a library for domain adaptation, a type of machine learning algorithm that re-purposes existing models to work in new domains. It is a fully-featured toolkit, allowing users to create a complete train/test pipeline in a few lines of code. It is also modular, so users can import just the parts they need, and not worry about being locked into a framework. One defining feature of this library is its customizability. In particular, complex training algorithms can be easily modified and combined, thanks to a system of composable, lazily-evaluated hooks. In this technical report, we explain in detail these features and the overall design of the library. Code is available at this https URL",
author = "Kevin Musgrave and Serge Belongie and Ser-Nam Lim",
year = "2022",
language = "English",
publisher = "arXiv.org",
type = "WorkingPaper",
institution = "arXiv.org",

}

RIS

TY - UNPB

T1 - PyTorch Adapt

AU - Musgrave, Kevin

AU - Belongie, Serge

AU - Lim, Ser-Nam

PY - 2022

Y1 - 2022

N2 - PyTorch Adapt is a library for domain adaptation, a type of machine learning algorithm that re-purposes existing models to work in new domains. It is a fully-featured toolkit, allowing users to create a complete train/test pipeline in a few lines of code. It is also modular, so users can import just the parts they need, and not worry about being locked into a framework. One defining feature of this library is its customizability. In particular, complex training algorithms can be easily modified and combined, thanks to a system of composable, lazily-evaluated hooks. In this technical report, we explain in detail these features and the overall design of the library. Code is available at this https URL

AB - PyTorch Adapt is a library for domain adaptation, a type of machine learning algorithm that re-purposes existing models to work in new domains. It is a fully-featured toolkit, allowing users to create a complete train/test pipeline in a few lines of code. It is also modular, so users can import just the parts they need, and not worry about being locked into a framework. One defining feature of this library is its customizability. In particular, complex training algorithms can be easily modified and combined, thanks to a system of composable, lazily-evaluated hooks. In this technical report, we explain in detail these features and the overall design of the library. Code is available at this https URL

M3 - Preprint

BT - PyTorch Adapt

PB - arXiv.org

ER -

ID: 384619158