We lifted 5 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| mxbonn/ltmp | canonical | 4 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| bipartite_soft_matching | Ran | mxbonn/ltmp/ltmp/token_merging.py pointer only (licence: NONE) · get_code("424049f15b90678d") |
| do_nothing | Ran | mxbonn/ltmp/ltmp/token_merging.py pointer only (licence: NONE) · get_code("969e8250d805a154") |
| kth_bipartite_soft_matching | Ran | mxbonn/ltmp/ltmp/token_merging.py pointer only (licence: NONE) · get_code("674be50d432e8846") |
| parse_r | Ran | mxbonn/ltmp/ltmp/utils.py pointer only (licence: NONE) · get_code("94e8f254ce489a4b") |
| make_visualization | Not yet run | mxbonn/ltmp/ltmp/vis.py pointer only (licence: NONE) · get_code("61c0c3039d046800") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Vision transformers have demonstrated remarkable success in a wide range of computer vision tasks over the last years. However, their high computational costs remain a significant barrier to their practical deployment. In particular, the complexity of transformer models is quadratic with respect to the number of input tokens. Therefore techniques that reduce the number of input tokens that need to be processed have been proposed. This paper introduces Learned Thresholds token Merging and Pruning (LTMP), a novel approach that leverages the strengths of both token merging and token pruning. LTMP uses learned threshold masking modules that dynamically determine which tokens to merge and which to prune. We demonstrate our approach with extensive experiments on vision transformers on the ImageNet classification task. Our results demonstrate that LTMP achieves state-of-the-art accuracy across reduction rates while requiring only a single fine-tuning epoch, which is an order of magnitude faster than previous methods. Code is available at https://github.com/Mxbonn/ltmp .
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2307.10780")
get_code_for_paper("2307.10780")
have("2307.10780")
Connect an agent — have() is free.