FusionBench: A Comprehensive Benchmark of Deep Model Fusion

• URL: http://arxiv.org/abs/2406.03280v3
• Date: Fri, 14 Jun 2024 07:19:51 GMT
• Title: FusionBench: A Comprehensive Benchmark of Deep Model Fusion
• Authors: Anke Tang, Li Shen, Yong Luo, Han Hu, Bo Du, Dacheng Tao,
• Abstract summary: Deep model fusion is a technique that unifies the predictions or parameters of several deep neural networks into a single model. FusionBench is the first comprehensive benchmark dedicated to deep model fusion.
• Score: 78.80920533793595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep model fusion is an emerging technique that unifies the predictions or parameters of several deep neural networks into a single model in a cost-effective and data-efficient manner. This enables the unified model to take advantage of the original models' strengths, potentially exceeding their performance. Although a variety of deep model fusion techniques have been introduced, their evaluations tend to be inconsistent and often inadequate to validate their effectiveness and robustness against distribution shifts. To address this issue, we introduce FusionBench, which is the first comprehensive benchmark dedicated to deep model fusion. FusionBench covers a wide range of tasks, including open-vocabulary image classification, text classification, and text-to-text generation. Each category includes up to eight tasks with corresponding task-specific models, featuring both full fine-tuning and LoRA fine-tuning, as well as models of different sizes, to ensure fair and balanced comparisons of various multi-task model fusion techniques across different tasks, model scales, and fine-tuning strategies. We implement and evaluate a broad spectrum of deep model fusion techniques. These techniques range from model ensemble methods, which combine the predictions to improve the overall performance, to model merging, which integrates different models into a single one, and model mixing methods, which upscale or recombine the components of the original models. FusionBench now contains 26 distinct tasks, 74 fine-tuned models, and 16 fusion techniques, and we are committed to consistently expanding the benchmark with more tasks, models, and fusion techniques. In addition, we offer a well-documented set of resources and guidelines to aid researchers in understanding and replicating the benchmark results. Homepage https://github.com/tanganke/fusion_bench

Related papers

• A Collaborative Ensemble Framework for CTR Prediction [73.59868761656317]
  We propose a novel framework, Collaborative Ensemble Training Network (CETNet), to leverage multiple distinct models. Unlike naive model scaling, our approach emphasizes diversity and collaboration through collaborative learning. We validate our framework on three public datasets and a large-scale industrial dataset from Meta.
  arXiv  Detail & Related papers  (2024-11-20T20:38:56Z)
• What Matters for Model Merging at Scale? [94.26607564817786]
  Model merging aims to combine multiple expert models into a more capable single model. Previous studies have primarily focused on merging a few small models. This study systematically evaluates the utility of model merging at scale.
  arXiv  Detail & Related papers  (2024-10-04T17:17:19Z)
• PLeaS -- Merging Models with Permutations and Least Squares [43.17620198572947]
  We propose a new two-step algorithm to merge models-termed PLeaS. PLeaS partially matches nodes in each layer by maximizing alignment. It computes the weights of the merged model as a layer-wise Least Squares solution.
  arXiv  Detail & Related papers  (2024-07-02T17:24:04Z)
• EMR-Merging: Tuning-Free High-Performance Model Merging [55.03509900949149]
  We show that Elect, Mask & Rescale-Merging (EMR-Merging) shows outstanding performance compared to existing merging methods. EMR-Merging is tuning-free, thus requiring no data availability or any additional training while showing impressive performance.
  arXiv  Detail & Related papers  (2024-05-23T05:25:45Z)
• Multimodal Fusion with Pre-Trained Model Features in Affective Behaviour Analysis In-the-wild [37.32217405723552]
  We present an approach for addressing the task of Expression (Expr) Recognition and Valence-Arousal (VA) Estimation. We evaluate the Aff-Wild2 database using pre-trained models, then extract the final hidden layers of the models as features. Following preprocessing and or convolution to align the extracted features, different models are employed for modal fusion.
  arXiv  Detail & Related papers  (2024-03-22T09:00:24Z)
• A Lightweight Feature Fusion Architecture For Resource-Constrained Crowd Counting [3.5066463427087777]
  We introduce two lightweight models to enhance the versatility of crowd-counting models. These models maintain the same downstream architecture while incorporating two distinct backbones: MobileNet and MobileViT. We leverage Adjacent Feature Fusion to extract diverse scale features from a Pre-Trained Model (PTM) and subsequently combine these features seamlessly.
  arXiv  Detail & Related papers  (2024-01-11T15:13:31Z)
• Deep Model Fusion: A Survey [37.39100741978586]
  Deep model fusion/merging is an emerging technique that merges the parameters or predictions of multiple deep learning models into a single one. It faces several challenges, including high computational cost, high-dimensional parameter space, interference between different heterogeneous models, etc.
  arXiv  Detail & Related papers  (2023-09-27T14:40:12Z)
• Dataless Knowledge Fusion by Merging Weights of Language Models [51.8162883997512]
  Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. This creates a barrier to fusing knowledge across individual models to yield a better single model. We propose a dataless knowledge fusion method that merges models in their parameter space.
  arXiv  Detail & Related papers  (2022-12-19T20:46:43Z)
• Multimodal E-Commerce Product Classification Using Hierarchical Fusion [0.0]
  The proposed method significantly outperformed the unimodal models' performance and the reported performance of similar models on our specific task. We did experiments with multiple fusing techniques and found, that the best performing technique to combine the individual embedding of the unimodal network is based on combining concatenation and averaging the feature vectors.
  arXiv  Detail & Related papers  (2022-07-07T14:04:42Z)
• Attention Bottlenecks for Multimodal Fusion [90.75885715478054]
  Machine perception models are typically modality-specific and optimised for unimodal benchmarks. We introduce a novel transformer based architecture that uses fusion' for modality fusion at multiple layers. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple audio-visual classification benchmarks.
  arXiv  Detail & Related papers  (2021-06-30T22:44:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.