1 code implementation • 31 Mar 2024 • Jiantao Wu, Shentong Mo, Sara Atito, ZhenHua Feng, Josef Kittler, Muhammad Awais
Recently, masked image modeling (MIM), an important self-supervised learning (SSL) method, has drawn attention for its effectiveness in learning data representation from unlabeled data.
no code implementations • 2 Dec 2023 • Jiantao Wu, Shentong Mo, Sara Atito, Josef Kittler, ZhenHua Feng, Muhammad Awais
Recently, self-supervised metric learning has raised attention for the potential to learn a generic distance function.
no code implementations • 22 Aug 2023 • Jiantao Wu, Shentong Mo, Muhammad Awais, Sara Atito, ZhenHua Feng, Josef Kittler
Self-supervised pretraining (SSP) has emerged as a popular technique in machine learning, enabling the extraction of meaningful feature representations without labelled data.
1 code implementation • 22 Mar 2023 • Jiantao Wu, Shentong Mo, Muhammad Awais, Sara Atito, Xingshen Zhang, Lin Wang, Xiang Yang
One major challenge of disentanglement learning with variational autoencoders is the trade-off between disentanglement and reconstruction fidelity.
no code implementations • 18 Aug 2022 • Jiantao Wu, Fabrizio Orlandi, Tarek Alskaif, Declan O'Sullivan, Soumyabrata Dev
In a decentralized household energy system comprised of various devices such as home appliances, electric vehicles, and solar panels, end-users are able to dig deeper into the system's details and further achieve energy sustainability if they are presented with data on the electric energy consumption and production at the granularity of the device.
no code implementations • 28 May 2022 • Jiantao Wu, Shentong Mo
Furthermore, we investigate the inter-object and intra-object relationship and find that the latter is crucial for self-supervised pre-training.
no code implementations • 8 Feb 2021 • Jiantao Wu, Lin Wang, Bo Yang, Fanqi Li, Chunxiuzi Liu, Jin Zhou
Disentanglement is a highly desirable property of representation owing to its similarity to human understanding and reasoning.
no code implementations • 17 Oct 2020 • Jiantao Wu, Lin Wang
Disentanglement is a highly desirable property of representation due to its similarity with human's understanding and reasoning.