Generative Adversarial Networks in the built environment: A comprehensive review of the application of GANs across data types and scales
Abstract Generative Adversarial Networks (GANs) are a type of deep neural network that
have achieved many state-of-the-art results for generative tasks. GANs can be useful in the …
have achieved many state-of-the-art results for generative tasks. GANs can be useful in the …
Guigan: Learning to generate gui designs using generative adversarial networks
T Zhao, C Chen, Y Liu, X Zhu - 2021 IEEE/ACM 43rd …, 2021 - ieeexplore.ieee.org
Graphical User Interface (GUI) is ubiquitous in almost all modern desktop software, mobile
applications and online websites. A good GUI design is crucial to the success of the software …
applications and online websites. A good GUI design is crucial to the success of the software …
Causal disentanglement for semantic-aware intent learning in recommendation
Traditional recommendation models trained on observational interaction data have
generated large impacts in a wide range of applications, it faces bias problems that cover …
generated large impacts in a wide range of applications, it faces bias problems that cover …
A multi-phase blending method with incremental intensity for training detection networks
Object detection is an important topic for visual data processing in the visual computing
area. Although a number of approaches have been studied, it still remains a challenge …
area. Although a number of approaches have been studied, it still remains a challenge …
Reconstructing 3D shapes from multiple sketches using direct shape optimization
3D shape reconstruction from multiple hand-drawn sketches is an intriguing way to 3D
shape modeling. Currently, state-of-the-art methods employ neural networks to learn a …
shape modeling. Currently, state-of-the-art methods employ neural networks to learn a …
BIM Library transplant: bridging human expertise and artificial intelligence for customized design detailing
This study introduces a framework for transplanting a building information modeling (BIM)
library. Design detailing constitutes 50%–60% of the total design time, even within the BIM …
library. Design detailing constitutes 50%–60% of the total design time, even within the BIM …
Counterfactual explanation for fairness in recommendation
Fairness-aware recommendation alleviates discrimination issues to build trustworthy
recommendation systems. Explaining the causes of unfair recommendations is critical, as it …
recommendation systems. Explaining the causes of unfair recommendations is critical, as it …
Reinforced path reasoning for counterfactual explainable recommendation
Counterfactual explanations interpret the recommendation mechanism by exploring how
minimal alterations on items or users affect recommendation decisions. Existing …
minimal alterations on items or users affect recommendation decisions. Existing …
Mgpolicy: Meta graph enhanced off-policy learning for recommendations
Off-policy learning has drawn huge attention in recommender systems (RS), which provides
an opportunity for reinforcement learning to abandon the expensive online training …
an opportunity for reinforcement learning to abandon the expensive online training …
Constrained off-policy learning over heterogeneous information for fairness-aware recommendation
Fairness-aware recommendation eliminates discrimination issues to build trustworthy
recommendation systems. Existing fairness-aware approaches ignore accounting for rich …
recommendation systems. Existing fairness-aware approaches ignore accounting for rich …