Search Results for author:
Found 5 papers, 2 papers with code
Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention
The learnable thresholds are optimized in budget-aware training to balance accuracy and complexity, performing the corresponding pruning configurations for different input instances.
Ranked #6 on
Efficient ViTs
on ImageNet-1K (With LV-ViT-S)
RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization
We are the first to propose the more constrained but hardware-friendly Power-of-Two quantization scheme for low-bit PTQ specially and prove that it can achieve nearly the same accuracy as SOTA PTQ method.
A free lunch from ViT:Adaptive Attention Multi-scale Fusion Transformer for Fine-grained Visual Recognition
Learning subtle representation about object parts plays a vital role in fine-grained visual recognition (FGVR) field.
Ranked #10 on
Fine-Grained Image Classification
on Stanford Dogs
Fine-Grained Image Classification
Fine-Grained Visual Recognition
AdaPruner: Adaptive Channel Pruning and Effective Weights Inheritance
While previous pruning methods have mostly focused on identifying unimportant channels, channel pruning is considered as a special case of neural architecture search in recent years.
An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution
We propose an efficient once-for-all budgeted pruning framework (OFARPruning) to find many compact network structures close to winner tickets in the early training stage considering the effect of input resolution during the pruning process.
