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Found 7 papers, 2 papers with code
Prompting-based Temporal Domain Generalization
Machine learning traditionally assumes that the training and testing data are distributed independently and identically.
HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous Denoising
The paper presents a novel approach for vector-floorplan generation via a diffusion model, which denoises 2D coordinates of room/door corners with two inference objectives: 1) a single-step noise as the continuous quantity to precisely invert the continuous forward process; and 2) the final 2D coordinate as the discrete quantity to establish geometric incident relationships such as parallelism, orthogonality, and corner-sharing.
Floorplan Restoration by Structure Hallucinating Transformer Cascades
This paper presents an extreme floorplan reconstruction task, a new benchmark for the task, and a neural architecture as a solution.
House-GAN++: Generative Adversarial Layout Refinement Network towards Intelligent Computational Agent for Professional Architects
This paper proposes a generative adversarial layout refinement network for automated floorplan generation.
House-GAN++: Generative Adversarial Layout Refinement Networks
This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation.
Distill-2MD-MTL: Data Distillation based on Multi-Dataset Multi-Domain Multi-Task Frame Work to Solve Face Related Tasksks, Multi Task Learning, Semi-Supervised Learning
We propose a new semi-supervised learning method on face-related tasks based on Multi-Task Learning (MTL) and data distillation.
Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks
In this paper, we show that finding an appropriate feature for the given problem may be still important as they can en- hance the performance of CNN-based algorithms.
