Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/164060
Title: | Latte: cross-framework Python package for evaluation of latent-based generative models | Authors: | Watcharasupat, Karn N. Lee, Junyoung Lerch, Alexander |
Keywords: | Engineering::Electrical and electronic engineering Engineering::Computer science and engineering |
Issue Date: | 2022 | Source: | 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 | Journal: | Software Impacts | 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. | URI: | https://hdl.handle.net/10356/164060 | ISSN: | 2665-9638 | DOI: | 10.1016/j.simpa.2022.100222 | 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/). | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
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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