TRAM: Bridging Trust Regions and Sharpness for Domain Generalization in Fine-Tuning

Alapfogalmak

TRAM integrates sharpness-aware minimization with trust region optimization to improve out-of-domain generalization during fine-tuning.

Kivonat

TRAM combines SAM and trust region methods for low curvature in both parameter and function spaces.
TRAM outperforms SAM- and TR-based optimization across various tasks.
TRAM establishes a new standard for domain-generalizable models with minimal additional computation.
Results show improved performance in image classification, language modeling, and cross-lingual transfer tasks.
TRAM-Fisher variant offers an efficient alternative inspired by Fisher SAM.

Statisztikák

Sharpness-aware minimization improves language model generalization.
TRAM introduces an awareness of function curvature into sharpness-aware minimization.
TRAM uses a trust region bound to inform the SAM adversarial neighborhood.

Idézetek

"TRAM establishes a novel standard in fine-tuning for domain-generalizable models."
"TRAM integrates representation smoothing regularization into sharpness-aware minimization."

Főbb Kivonatok

TRAM

by Tom Sherborn... : arxiv.org 03-13-2024

https://arxiv.org/pdf/2310.03646.pdf

Mélyebb kérdések

How does TRAM address the limitations of existing SAM-style optimization methods

TRAM addresses the limitations of existing SAM-style optimization methods by integrating trust region awareness into the optimization process. While SAM focuses on minimizing sharpness in parameter space, TRAM goes a step further by considering smoothness in both parameter and function spaces. By incorporating trust region measurements, TRAM ensures that the model optimizes towards flatter minima while also maintaining low local curvature between output representations. This approach allows TRAM to better leverage pre-trained structures during fine-tuning, leading to improved generalization across different distributions.

What are the implications of TRAM's success for future research in machine learning optimization

The success of TRAM has significant implications for future research in machine learning optimization. Firstly, it highlights the importance of considering not just sharpness in parameter space but also smoothness in function space for optimal model performance. This holistic approach could inspire new optimization algorithms that combine multiple strategies to enhance generalization capabilities. Additionally, TRAM's ability to improve adaptation to out-of-distribution scenarios suggests that similar techniques could be applied to various real-world applications where robust domain transfer is crucial.

How can the principles behind TRAM be applied to other domains beyond image classification and language modeling

The principles behind TRAM can be applied beyond image classification and language modeling to other domains within machine learning and artificial intelligence. For example:

Speech Recognition: Optimizing speech recognition models using TRAM could lead to better adaptation across different accents or languages.
Recommendation Systems: Applying TRAM techniques could enhance recommendation systems' ability to generalize well when faced with new user preferences or items.
Healthcare AI: Utilizing TRAM in healthcare AI models may improve their performance when transferring knowledge between different medical specialties or patient populations.
By incorporating trust region awareness and focusing on both parameter and function space smoothness, these applications can benefit from enhanced generalization capabilities similar to those demonstrated by TRAM in image classification and language modeling tasks.