no code implementations • 17 May 2023 • Hao Lan Zhang, Yun Xue, Yifan Lu, Sanghyuk Lee
Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), digital twin, Metaverse and other related digital technologies have attracted much attention in recent years.
no code implementations • 21 Apr 2023 • Sanghyuk Lee, SeungHyun Lee, Byung Cheol Song
As a result, the proposed method is able to deal with more examples in the adaptation process than inductive ones, which can result in better classification performance of the model.
no code implementations • 31 Mar 2023 • Mart van Baalen, Andrey Kuzmin, Suparna S Nair, Yuwei Ren, Eric Mahurin, Chirag Patel, Sundar Subramanian, Sanghyuk Lee, Markus Nagel, Joseph Soriaga, Tijmen Blankevoort
We theoretically show the difference between the INT and FP formats for neural networks and present a plethora of post-training quantization and quantization-aware-training results to show how this theory translates to practice.
1 code implementation • 20 Oct 2021 • Sanghyuk Lee, SeungHyun Lee, Byung Cheol Song
Experimental results show that CxGrad effectively encourages the backbone to learn task-specific knowledge in the inner-loop and improves the performance of MAML up to a significant margin in both same- and cross-domain few-shot classification.
no code implementations • 3 Jun 2019 • Jaehoon Cha, Kyeong Soo Kim, Sanghyuk Lee
Conventional application of convolutional neural networks (CNNs) for image classification and recognition is based on the assumption that all target classes are equal(i. e., no hierarchy) and exclusive of one another (i. e., no overlap).
no code implementations • 24 Jan 2019 • Jaehoon Cha, Kyeong Soo Kim, Sanghyuk Lee
Noting the importance of the latent variables in inference and learning, we propose a novel framework for autoencoders based on the homeomorphic transformation of latent variables, which could reduce the distance between vectors in the transformed space, while preserving the topological properties of the original space, and investigate the effect of the latent space transformation on learning generative models and denoising corrupted data.
no code implementations • 17 Oct 2018 • Zhenghang Zhong, Zhe Tang, Xiangxing Li, Tiancheng Yuan, Yang Yang, Meng Wei, Yuanyuan Zhang, Renzhi Sheng, Naomi Grant, Chongfeng Ling, Xintao Huan, Kyeong Soo Kim, Sanghyuk Lee
In this paper, we present a new location fingerprinting database comprised of Wi-Fi received signal strength (RSS) and geomagnetic field intensity measured with multiple devices at a multi-floor building in Xi'an Jiatong-Liverpool University, Suzhou, China.
2 code implementations • 6 Dec 2017 • Kyeong Soo Kim, Sanghyuk Lee, Kaizhu Huang
Exploiting the hierarchical nature of the building/floor estimation and floor-level coordinates estimation of a location, we propose a new DNN architecture consisting of a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification of building/floor/location, on which the multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting is built.
2 code implementations • 3 Oct 2017 • Kyeong Soo Kim, Ruihao Wang, Zhenghang Zhong, Zikun Tan, Haowei Song, Jaehoon Cha, Sanghyuk Lee
One of key technologies for future large-scale location-aware services in access is a scalable indoor localization technique.
Networking and Internet Architecture C.2.1; I.2.6; I.5.1; I.5.2; I.5.4; I.5.5