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https://hdl.handle.net/10356/164060
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DC Field | Value | Language |
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dc.contributor.author | Watcharasupat, Karn N. | en_US |
dc.contributor.author | Lee, Junyoung | en_US |
dc.contributor.author | Lerch, Alexander | en_US |
dc.date.accessioned | 2023-01-04T01:01:46Z | - |
dc.date.available | 2023-01-04T01:01:46Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Watcharasupat, K. N., Lee, J. & Lerch, A. (2022). Latte: cross-framework Python package for evaluation of latent-based generative models. Software Impacts, 11, 100222-. https://dx.doi.org/10.1016/j.simpa.2022.100222 | en_US |
dc.identifier.issn | 2665-9638 | en_US |
dc.identifier.uri | https://hdl.handle.net/10356/164060 | - |
dc.description.abstract | Latte (for LATent Tensor Evaluation) is a Python library for evaluation of latent-based generative models in the fields of disentanglement learning and controllable generation. Latte is compatible with both PyTorch and TensorFlow/Keras, and provides both functional and modular APIs that can be easily extended to support other deep learning frameworks. Using NumPy-based and framework-agnostic implementation, Latte ensures reproducible, consistent, and deterministic metric calculations regardless of the deep learning framework of choice. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Software Impacts | en_US |
dc.rights | © 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | en_US |
dc.subject | Engineering::Electrical and electronic engineering | en_US |
dc.subject | Engineering::Computer science and engineering | en_US |
dc.title | Latte: cross-framework Python package for evaluation of latent-based generative models | en_US |
dc.type | Journal Article | en |
dc.contributor.school | School of Electrical and Electronic Engineering | en_US |
dc.identifier.doi | 10.1016/j.simpa.2022.100222 | - |
dc.description.version | Published version | en_US |
dc.identifier.scopus | 2-s2.0-85123614570 | - |
dc.identifier.volume | 11 | en_US |
dc.identifier.spage | 100222 | en_US |
dc.subject.keywords | Deep Generative Networks | en_US |
dc.subject.keywords | Disentanglement Learning | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
Appears in Collections: | EEE Journal Articles |
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File | Description | Size | Format | |
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1-s2.0-S2665963822000033-main.pdf | 498.78 kB | Adobe PDF | View/Open |
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