Model Selection with Model Zoo via Graph Learning

• URL: http://arxiv.org/abs/2404.03988v1
• Date: Fri, 5 Apr 2024 09:50:00 GMT
• Title: Model Selection with Model Zoo via Graph Learning
• Authors: Ziyu Li, Hilco van der Wilk, Danning Zhan, Megha Khosla, Alessandro Bozzon, Rihan Hai,
• Abstract summary: We introduce TransferGraph, a novel framework that reformulates model selection as a graph learning problem. We demonstrate TransferGraph's effectiveness in capturing essential model-dataset relationships, yielding up to a 32% improvement in correlation between predicted performance and the actual fine-tuning results compared to the state-of-the-art methods.
• Score: 45.30615308692713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pre-trained deep learning (DL) models are increasingly accessible in public repositories, i.e., model zoos. Given a new prediction task, finding the best model to fine-tune can be computationally intensive and costly, especially when the number of pre-trained models is large. Selecting the right pre-trained models is crucial, yet complicated by the diversity of models from various model families (like ResNet, Vit, Swin) and the hidden relationships between models and datasets. Existing methods, which utilize basic information from models and datasets to compute scores indicating model performance on target datasets, overlook the intrinsic relationships, limiting their effectiveness in model selection. In this study, we introduce TransferGraph, a novel framework that reformulates model selection as a graph learning problem. TransferGraph constructs a graph using extensive metadata extracted from models and datasets, while capturing their inherent relationships. Through comprehensive experiments across 16 real datasets, both images and texts, we demonstrate TransferGraph's effectiveness in capturing essential model-dataset relationships, yielding up to a 32% improvement in correlation between predicted performance and the actual fine-tuning results compared to the state-of-the-art methods.

Related papers

• Knowledge Fusion By Evolving Weights of Language Models [5.354527640064584]
  This paper examines the approach of integrating multiple models into a unified model. We propose a knowledge fusion method named Evolver, inspired by evolutionary algorithms.
  arXiv  Detail & Related papers  (2024-06-18T02:12:34Z)
• A Two-Phase Recall-and-Select Framework for Fast Model Selection [13.385915962994806]
  We propose a two-phase (coarse-recall and fine-selection) model selection framework. It aims to enhance the efficiency of selecting a robust model by leveraging the models' training performances on benchmark datasets. It has been demonstrated that the proposed methodology facilitates the selection of a high-performing model at a rate about 3x times faster than conventional baseline methods.
  arXiv  Detail & Related papers  (2024-03-28T14:44:44Z)
• Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach [25.927323251675386]
  We leverage the meta-features associated with each entity as a source of worldly knowledge and employ entity representations from the models. We propose using the consistency between these representations and the meta-features as a metric for evaluating pre-trained models. Our method's effectiveness is demonstrated across various domains, including models with relational datasets, large language models and image models.
  arXiv  Detail & Related papers  (2024-01-02T17:08:26Z)
• Revealing the Underlying Patterns: Investigating Dataset Similarity, Performance, and Generalization [0.0]
  Supervised deep learning models require significant amount of labeled data to achieve an acceptable performance on a specific task. We establish image-image, dataset-dataset, and image-dataset distances to gain insights into the model's behavior.
  arXiv  Detail & Related papers  (2023-08-07T13:35:53Z)
• TRAK: Attributing Model Behavior at Scale [79.56020040993947]
  We present TRAK (Tracing with Randomly-trained After Kernel), a data attribution method that is both effective and computationally tractable for large-scale, differenti models.
  arXiv  Detail & Related papers  (2023-03-24T17:56:22Z)
• Knowledge is a Region in Weight Space for Fine-tuned Language Models [48.589822853418404]
  We study how the weight space and the underlying loss landscape of different models are interconnected. We show that language models that have been finetuned on the same dataset form a tight cluster in the weight space, while models finetuned on different datasets from the same underlying task form a looser cluster.
  arXiv  Detail & Related papers  (2023-02-09T18:59:18Z)
• 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)
• Comparing Test Sets with Item Response Theory [53.755064720563]
  We evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. We find that Quoref, HellaSwag, and MC-TACO are best suited for distinguishing among state-of-the-art models. We also observe span selection task format, which is used for QA datasets like QAMR or SQuAD2.0, is effective in differentiating between strong and weak models.
  arXiv  Detail & Related papers  (2021-06-01T22:33:53Z)
• Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics [118.75207687144817]
  We introduce Data Maps, a model-based tool to characterize and diagnose datasets. We leverage a largely ignored source of information: the behavior of the model on individual instances during training. Our results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization.
  arXiv  Detail & Related papers  (2020-09-22T20:19:41Z)

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.