Summary of Fusionbench: a Comprehensive Benchmark Of Deep Model Fusion, by Anke Tang et al.
FusionBench: A Comprehensive Benchmark of Deep Model Fusion
by Anke Tang, Li Shen, Yong Luo, Han Hu, Bo Du, Dacheng Tao
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep model fusion is a technique that combines the predictions or parameters of multiple deep neural networks into a single model. This enables the unified model to leverage the strengths of the original models, potentially exceeding their performance. The evaluation of various deep model fusion techniques has been inconsistent and inadequate, hindering the validation of their effectiveness and robustness against distribution shifts. To address this issue, FusionBench is introduced as the first comprehensive benchmark dedicated to deep model fusion. It covers a wide range of tasks, including open-vocabulary image classification, text classification, and text-to-text generation, featuring models of different sizes and fine-tuning strategies. A broad spectrum of deep model fusion techniques are implemented and evaluated, ranging from model ensemble methods to model merging and model mixing methods. FusionBench now contains 26 distinct tasks, 74 fine-tuned models, and 16 fusion techniques, with plans for continued expansion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to combine the strengths of multiple artificial intelligence (AI) models into one super-powerful model. That’s what deep model fusion is all about! It’s a new technique that helps AI systems work better together by combining their predictions or parameters. Currently, there are many different ways to do this, but it’s hard to know which method works best. To solve this problem, researchers created a special benchmark called FusionBench. This benchmark tests and compares many different deep model fusion techniques on various tasks like image recognition, text classification, and language translation. By using FusionBench, AI developers can learn from the results and create even better models in the future. |
Keywords
» Artificial intelligence » Fine tuning » Image classification » Text classification » Text generation » Translation
