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Dehua Tang

2 accepted papers

2024

DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization

NeurIPS 2024poster

Diffusion models have achieved remarkable progress in the field of image generation due to their outstanding capabilities. However, these models require substantial computing resources because of the multi-step denoising process during inference. While traditional pruning methods have been employed…

Cited by 2SourcePDFScholar
2024

UPDP: A Unified Progressive Depth Pruner for CNN and Vision Transformer

AAAI 2024technical

Traditional channel-wise pruning methods by reducing network channels struggle to effectively prune efficient CNN models with depth-wise convolutional layers and certain efficient modules, such as popular inverted residual blocks. Prior depth pruning methods by reducing network depths are not suitab…

Cited by 13SourcePDFScholar