The field of artificial intelligence is witnessing a significant shift towards the utilization of synthetic data, driven by the need to overcome data scarcity and privacy concerns. Researchers are developing innovative methods for generating high-fidelity synthetic data that can mimic real-world statistics while preserving privacy. These advancements have far-reaching implications for various AI applications, including healthcare, computer vision, and urban scene understanding. Notably, the integration of synthetic data with real-world data is being explored as a means to create robust and efficient models. The development of new frameworks and architectures, such as those incorporating uncertainty-aware control mechanisms and sparse-dense graph architectures, is enabling the creation of high-quality synthetic data that can support sensitive AI applications.
Some noteworthy papers in this regard include: A Hybrid Machine Learning Approach for Synthetic Data Generation, which introduces a novel hybrid framework for high-fidelity healthcare data synthesis. sketch2symm: Symmetry-aware sketch-to-shape generation, which proposes a two-stage generation method that produces geometrically consistent 3D shapes from sketches. Uncertainty-Aware ControlNet: Bridging Domain Gaps with Synthetic Image Generation, which introduces a method to utilize data from unlabeled domains to train ControlNets by introducing the concept of uncertainty into the control mechanism. A Framework for Low-Effort Training Data Generation for Urban Semantic Segmentation, which presents a new framework that adapts an off-the-shelf diffusion model to a target domain using only imperfect pseudo-labels. SDGraph: Multi-Level Sketch Representation Learning, which develops a deep learning architecture designed to exploit effective information across multiple levels of sketch representation. CymbaDiff: Structured Spatial Diffusion for Sketch-based 3D Semantic Urban Scene Generation, which proposes a method that significantly enhances spatial coherence in outdoor 3D scene generation.
