ERM++: An Improved Baseline for Domain Generalization

Kernekoncepter

Hyperparameter-tuned ERM training procedure, ERM++, enhances Domain Generalization performance significantly.

Resumé

Domain Generalization (DG) measures a classifier's ability to generalize to new data distributions.
ERM++ improves DG performance by tuning additional hyperparameters beyond ERM.
Training Amount, Initialization, and Regularization are key components of ERM++.
ERM++ outperforms prior ERM baselines and SOTA methods on DomainBed datasets.
Similarity to pre-training data influences DG performance, but ERM++ with strong initializations can perform well on dissimilar datasets.
ERM++ computational cost is lower compared to other methods.
Pre-training data similarity plays a crucial role in DG performance.
ERM++ serves as a strong baseline for future research in DG.

Statistik

ERM has achieved strong results by tuning hyper-parameters such as learning rate, weight decay, batch size, and dropout.
ERM++ improves DG performance by over 5% compared to prior ERM baselines.
ERM++ outperforms all SOTA methods on DomainBed datasets.

Citater

"We focus on tuning previously untuned hyper-parameters, including training amount, initialization, and additional regularizers."
"ERM++ improves the performance of DG by over 5% compared to prior ERM baselines."

Vigtigste indsigter udtrukket fra

ERM++

by Piotr Teterw... kl. arxiv.org 03-28-2024

https://arxiv.org/pdf/2304.01973.pdf

Dybere Forespørgsler

How does ERM++ compare to other baseline methods in terms of computational efficiency

ERM++ demonstrates superior computational efficiency compared to other baseline methods in Domain Generalization. While methods like DIWA and MIRO require extensive hyperparameter searches and ensemble training, ERM++ achieves state-of-the-art results with reasonable default hyperparameters. This efficiency is attributed to the simple yet effective tuning of hyperparameters like learning rate, weight decay, batch size, and dropout, along with additional tuning of previously untuned parameters like training amount, initialization, and regularizers. By automating the selection of training length and learning rate schedule, ERM++ optimizes the training process without the need for extensive computational resources.

What are the implications of the findings on the importance of pre-training data similarity for DG

The findings on the importance of pre-training data similarity for Domain Generalization (DG) have significant implications for model performance. The study reveals that the similarity between pre-training data and target domains plays a crucial role in DG performance. Higher text and image similarity between pre-training data and target datasets lead to improved performance in DG tasks. This suggests that leveraging pre-training data that closely aligns with the target domains can enhance model generalization capabilities. However, the study also highlights that strong initializations like DINOv2 can deliver high performance even on dissimilar datasets, showcasing the importance of pre-training methodology in complementing data composition for DG tasks.

How can the principles of ERM++ be applied to other machine learning tasks beyond Domain Generalization

The principles of ERM++ can be applied to various machine learning tasks beyond Domain Generalization to enhance model performance and efficiency.

Transfer Learning: The tuning of hyperparameters, such as training length, initialization, and regularization, can be applied to transfer learning tasks to improve model adaptation to new domains or tasks.
Image Classification: ERM++ principles can be utilized in image classification tasks to optimize model training and prevent overfitting, leading to better generalization to unseen data distributions.
Natural Language Processing: Similar hyperparameter tuning strategies can be employed in NLP tasks to fine-tune language models for improved performance on diverse datasets.
Computer Vision: The concept of leveraging pre-training data similarity and optimizing training procedures can benefit computer vision tasks like object detection, segmentation, and image generation, enhancing model robustness and generalization capabilities.