no code implementations • 28 Jun 2023 • Wenjing Huang, Shikui Tu, Lei Xu
Diffusion models have showcased their remarkable capability to synthesize diverse and high-quality images, sparking interest in their application for real image editing.
1 code implementation • 24 Mar 2022 • Wenjing Huang, Shikui Tu, Lei Xu
To strike a balance between the reconstruction capacity and the control flexibility, the encoder is designed as a multi-head structure to yield embeddings for reconstruction and control, respectively: a high-dimensional tensor with spatial properties for consistent reconstruction and four low-dimensional facial component embeddings for semantic face editing.
no code implementations • 1 Jul 2021 • Zhigang Li, Wenjing Huang, J. H. Zheng, Q. H. Wu
Although DC and linearized AC power flow equations are typically used to model dispatchable regions for transmission systems, these equations are rarely suitable for distribution networks.
no code implementations • 14 Jun 2021 • Rui Su, Wenjing Huang, Haoyu Ma, Xiaowei Song, Jinglu Hu
Compared with object detection of static images, video object detection is more challenging due to the motion of objects, while providing rich temporal information.
no code implementations • 25 Sep 2019 • Wenjing Huang, Shikui Tu, Lei Xu
In the inference time, when given an input, we will start a search process in the latent space which aims to find the closest reconstruction to the given image on the distribution of normal data.
no code implementations • 12 Apr 2019 • Wenjing Huang, Shikui Tu, Lei Xu
Proposed in 1991, Least Mean Square Error Reconstruction for self-organizing network, shortly Lmser, was a further development of the traditional auto-encoder (AE) by folding the architecture with respect to the central coding layer and thus leading to the features of symmetric weights and neurons, as well as jointly supervised and unsupervised learning.