Practical Transferability Estimation for Image Classification Tasks

Alapfogalmak

The author proposes a practical transferability metric, JC-NCE score, to improve the robustness of task difference estimation in transfer learning. By considering both sample and label distances, the JC-NCE score outperforms existing metrics.

Kivonat

The paper introduces the concept of transferability estimation in transfer learning for image classification tasks. It addresses the challenge of robust estimation under cross-domain cross-task settings. The proposed JC-NCE score improves performance by considering joint correspondences between source and target data. Extensive experiments validate its superiority over existing metrics.
Transfer learning is crucial for leveraging pretrained models on new tasks with limited labeled data. Source model selection based solely on accuracy may not guarantee high transfer accuracy due to task differences. Analytical transferability metrics offer efficient solutions but face limitations like heavy computation burdens or strict data assumptions.
The OTCE score considers domain and task differences but requires auxiliary tasks, leading to efficiency overhead. In contrast, the JC-NCE score eliminates the need for auxiliary tasks by improving task difference estimation robustness through optimal transport problem solving. This approach enhances transfer performance significantly in intra-dataset and inter-dataset settings.
The study compares JC-NCE with other metrics like LEEP, NCE, and H-score across various experimental configurations. Results show that JC-NCE consistently outperforms existing methods in estimating transferability accurately. Future research aims to explore applications of JC-NCE in heterogeneous transfer learning and multi-task scenarios.

Statisztikák

Recent analytical transferability metrics have been widely used for source model selection and multi-task learning.
The proposed JC-NCE score outperforms the auxiliary-task free version of OTCE for 7% and 12%, respectively.
Extensive validations demonstrate that JC-NCE is more robust than other existing transferability metrics on average.

Idézetek

"It is unreliable to perform source model selection according to the source model accuracy."
"Our JC-NCE score can predict the transfer performance more accurately."
"JC-NCE produces a more reasonable coupling result between source and target data."

Főbb Kivonatok

Practical Transferability Estimation for Image Classification Tasks

by Yang Tan,Yan... : arxiv.org 03-01-2024

https://arxiv.org/pdf/2106.10479.pdf

Mélyebb kérdések

How can the concept of transferability be applied beyond image classification tasks

Transferability can be applied beyond image classification tasks in various domains of machine learning. For example, in natural language processing (NLP), transfer learning techniques like BERT and GPT have shown significant improvements by transferring knowledge from pre-trained models to downstream tasks. The concept of transferability can also be utilized in reinforcement learning, where policies learned in one environment can be transferred or adapted to new environments with similar characteristics. Additionally, in healthcare applications, transfer learning can help leverage labeled data from one medical domain to improve performance on related but different medical tasks.

What are potential counterarguments against using analytical transferability metrics like JC-NCE

Potential counterarguments against using analytical transferability metrics like JC-NCE could include concerns about the generalizability of the metric across diverse datasets and task configurations. Critics might argue that while JC-NCE shows promising results in specific experimental setups, its effectiveness may vary when applied to real-world scenarios with more complex data distributions and task relationships. Another counterargument could focus on the interpretability of the metric - some researchers may question how well JC-NCE captures all relevant factors influencing transfer performance compared to more traditional approaches.

How might advancements in transferability estimation impact other areas of machine learning research

Advancements in transferability estimation have the potential to impact other areas of machine learning research significantly:

Improved Generalization: Better understanding and quantification of transferability could lead to enhanced generalization capabilities across different domains and tasks.

Efficient Model Selection: More accurate estimation of model transfer performance could streamline model selection processes by identifying source models that are most likely to perform well on target tasks.

Enhanced Multi-Task Learning: Transferability metrics can aid in optimizing multi-task learning frameworks by prioritizing tasks based on their compatibility with shared representations learned from other tasks.

Domain Adaptation: Advancements in estimating transferability could facilitate better adaptation between different domains, enabling models trained on one domain to perform effectively on another without extensive retraining.

Robustness Improvement: By understanding which factors contribute most significantly to successful transfers, researchers can develop strategies for improving model robustness and reducing negative transfers between related but distinct tasks or datasets.

These advancements have the potential not only to enhance current machine learning practices but also open up new avenues for research and application development across a wide range of fields within artificial intelligence and beyond.