Search Results for author:
Found 4 papers, 1 papers with code
DisDet: Exploring Detectability of Backdoor Attack on Diffusion Models
In this paper, for the first time, we systematically explore the detectability of the poisoned noise input for the backdoored diffusion models, an important performance metric yet little explored in the existing works.
ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks
Low-rank compression, a popular model compression technique that produces compact convolutional neural networks (CNNs) with low rankness, has been well-studied in the literature.
COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models
Attention-based vision models, such as Vision Transformer (ViT) and its variants, have shown promising performance in various computer vision tasks.
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks
By interpreting automatic rank selection from an architecture search perspective, we develop an end-to-end solution to determine the suitable layer-wise ranks in a differentiable and hardware-aware way.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
