Unfortunately, collecting high-quality and diverse data is both expensive and time-consuming. To mitigate this issue, we propose a novel Star-Agents framework, which automates the enhancement of data quality across datasets through multi-agent collaboration and assessment. The framework adopts a three-pronged strategy.

Large Reconstruction Models have made significant strides in the realm of automated 3D content generation from single or multiple input images.

We therefore explore training amortized models with noisy labels, and we find that this is inexpensive and surprisingly effective.

We consider nonconvex stochastic optimization problems in the asynchronous centralized distributed setup where the communication times from workers to a server can not be ignored, and the computation and communication times are potentially different for all workers.