Search Results for author:
Found 6 papers, 3 papers with code
RealisDance-DiT: Simple yet Strong Baseline towards Controllable Character Animation in the Wild
In this paper, we propose a new perspective that, as long as the foundation model is powerful enough, straightforward model modifications with flexible fine-tuning strategies can largely address the above challenges, taking a step towards controllable character animation in the wild.
Computation Rate Maximization for Wireless Powered Edge Computing With Multi-User Cooperation
Simulation results show that the performance of the proposed algorithms is comparable to that of the exhaustive search method, and the deep learning-based algorithm significantly reduces the execution time of the algorithm.
Adversarial Score Distillation: When score distillation meets GAN
Existing score distillation methods are sensitive to classifier-free guidance (CFG) scale: manifested as over-smoothness or instability at small CFG scales, while over-saturation at large ones.
Communication Efficiency Optimization of Federated Learning for Computing and Network Convergence of 6G Networks
In this paper, to improve the communication efficiency of federated learning in complex networks, we study the communication efficiency optimization of federated learning for computing and network convergence of 6G networks, methods that gives decisions on its training process for different network conditions and arithmetic power of participating devices in federated learning.
Super-Resolution Neural Operator
We propose Super-resolution Neural Operator (SRNO), a deep operator learning framework that can resolve high-resolution (HR) images at arbitrary scales from the low-resolution (LR) counterparts.
Mutual Information-Based Unsupervised Feature Transformation for Heterogeneous Feature Subset Selection
Conventional mutual information (MI) based feature selection (FS) methods are unable to handle heterogeneous feature subset selection properly because of data format differences or estimation methods of MI between feature subset and class label.
