Token Merging (ToMe) emerged as a promising solution to accelerate off-the-shelf Vision Trnansformers without training. However, it can suffer from accuracy ...
Mar 4, 2023 · Token merging has emerged as a new paradigm that can accelerate the inference of Vision Transformers (ViTs) without any retraining or fine-tuning.
Oct 31, 2022 · Our framework prunes Transformers in less than 3 minutes on a single GPU, which is over two orders of magnitude faster than existing pruning approaches that ...
By avoiding expensive retraining, the end-to-end pruning pipeline can be extremely fast and simplified, which typically takes a few minutes without any user ...
This work proposes a fast post-training pruning framework for Transformers that prunes Transformers in less than 3 minutes on a single GPU.
Oct 17, 2022 · By avoiding expensive retraining, the end-to-end pruning pipeline can be extremely fast and simplified, which typically takes a few minutes.
Apr 3, 2024 · To address this, we propose a fast posttraining pruning framework for Transformers that does not require any retraining. Given a resource ...
Inspired by post-training quantization (PTQ) toolkits, we propose a post-training pruning framework tailored for Transformers.
Missing: Compression Vision
It outputs pruned Transformer models satisfying the FLOPs/latency constraints within much less time (e.g., ⇠3 minutes), without user intervention. To address ...
Apr 4, 2024 · Our comprehensive experimental evaluation demonstrates that these methods facilitate a balanced compromise between model accuracy and ...
Missing: Fast | Show results with:Fast