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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