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Efficient Two-Phase Framework for Selecting High-Performing Pre-Trained Models
核心概念
A two-phase framework that efficiently identifies high-performing pre-trained models for a target task by leveraging model clustering and convergence trend mining.
要約
The paper proposes a two-phase framework for efficiently selecting high-performing pre-trained models for a target task:
Coarse-Recall Phase:
Constructs a performance matrix by fine-tuning each pre-trained model on a set of benchmark datasets.
Clusters the pre-trained models based on their performance on the benchmark datasets.
Computes a lightweight proxy score between the target dataset and the representative model of each cluster to recall a smaller subset of promising models.
Fine-Selection Phase:
Fine-tunes the recalled models on the target dataset.
Mines the convergence trends of the fine-tuned models on the benchmark datasets to predict their final performance on the target dataset.
Filters out poorly-performing models at early training stages based on the predicted performance.
The proposed framework significantly improves the efficiency of model selection compared to conventional methods, achieving a 3x speedup over successive halving and 5x over brute-force approaches, while maintaining near-optimal model performance.
A Two-Phase Recall-and-Select Framework for Fast Model Selection
統計
The paper reports the following key metrics:
Average accuracy of top K recalled models on target datasets
Total training epochs consumed by different model selection methods
引用
"The growing repository, though providing potential for improved task initialization, yet escalates the challenge of pinpointing the optimal model."
"We propose a two-phase (coarse-recall and fine-selection) model selection framework, aiming to enhance the efficiency of selecting a robust model by leveraging the models' training performances on benchmark datasets."
"Through extensive experimentation on tasks covering natural language processing and computer vision, 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."
深掘り質問
How can the proposed framework be extended to handle dynamic model repositories, where new models are continuously added over time
To extend the proposed framework to handle dynamic model repositories with continuously added models, we can implement a periodic update mechanism. This mechanism would involve regularly updating the performance matrix with the training results of new models on benchmark datasets. By periodically re-clustering the models based on their updated performance vectors, we can ensure that the model clusters remain relevant and reflective of the current model landscape. Additionally, the proxy scores for the new models can be computed and integrated into the recall phase to include them in the model selection process. This way, the framework can adapt to the evolving model repository and continue to efficiently select high-performing models from the dynamic pool.
What are the potential limitations or drawbacks of relying on benchmark dataset performance to cluster and predict the target task performance
While relying on benchmark dataset performance for clustering and predicting target task performance offers several advantages, there are potential limitations and drawbacks to consider. One limitation is the assumption that models performing well on benchmark datasets will also excel on the target task, which may not always hold true due to domain-specific nuances and dataset biases. Additionally, benchmark datasets may not fully represent the complexities of real-world tasks, leading to suboptimal model selection based solely on benchmark performance. Moreover, the clustering based on benchmark performance may overlook unique characteristics of individual models that could be beneficial for specific target tasks. Therefore, the framework may face challenges in accurately predicting performance on diverse and novel tasks that deviate significantly from the benchmark datasets.
How can the framework be adapted to handle regression or other non-classification tasks, where the performance metric may not be as straightforward as accuracy
Adapting the framework to handle regression or other non-classification tasks requires modifications to the performance metrics and clustering methods. For regression tasks, the framework can utilize metrics such as Mean Squared Error (MSE) or Root Mean Squared Error (RMSE) instead of accuracy. The clustering algorithm can be adjusted to consider the model's performance based on regression metrics on benchmark datasets. In the fine-selection phase, convergence trends can be mined based on regression performance to predict the final performance on the target regression task. By incorporating regression-specific metrics and methodologies, the framework can effectively handle regression tasks and other non-classification tasks, ensuring accurate model selection for a broader range of machine learning applications.
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