Towards Optimal Adapter Placement for Efficient Transfer Learning
Aleksandra I. Nowak, Otniel-Bogdan Mercea, Anurag Arnab, Jonas, Pfeiffer, Yann Dauphin, Utku Evci
TL;DR
This paper explores how the placement of adapters within pre-trained models affects transfer learning performance, proposing an expanded search space and revealing that strategic placement can match or surpass full adapter deployment.
Contribution
It introduces an extended search space for adapter placement, demonstrating that strategic and even random placements can achieve high performance, highlighting the importance of placement in PETL.
Findings
Adapter placement significantly impacts performance.
Random placements within the expanded space often perform well.
Strategic placement can match or outperform full adapter addition.
Abstract
Parameter-efficient transfer learning (PETL) aims to adapt pre-trained models to new downstream tasks while minimizing the number of fine-tuned parameters. Adapters, a popular approach in PETL, inject additional capacity into existing networks by incorporating low-rank projections, achieving performance comparable to full fine-tuning with significantly fewer parameters. This paper investigates the relationship between the placement of an adapter and its performance. We observe that adapter location within a network significantly impacts its effectiveness, and that the optimal placement is task-dependent. To exploit this observation, we introduce an extended search space of adapter connections, including long-range and recurrent adapters. We demonstrate that even randomly selected adapter placements from this expanded space yield improved results, and that high-performing placements…
Peer Reviews
Decision·Submitted to ICLR 2025
1. The designs for the adapter in Equation 2 and the recurrent searching graph appear solid. It seems that using the recurrent approach is more effective than the parallel sequential or long-range approach. 2. Based on experiments, a single, well-placed adapter can significantly improve performance compared to the linear probe.
I think the paper could benefit if the author addresses the following question related to multiple adapters and the corresponding search and replacement algorithms: 1. How many adapters would achieve optimal performance? From Section 5.2, it appears that the authors aim to use multiple adapters with random search to improve transfer learning quality, yet it remains unclear how many adapters are necessary to achieve robust performance coverage. Based on Figure 6 and the GGA section, performance
* The motivation is clear and the adapter placement is worth researching. * The proposed Gradient Guided Adapters (GGA) algorithm is interesting.
* The recurrent adapters require two forward processes, further deteriorating the efficiency of pre-trained models. * Results in table 1 lack the comparison with related works. * The paper lacks a clear and actionable conclusion. While the findings are interesting, it remains unclear how these results can be directly applied to practical scenarios.
1. The authors introduced a novel extension of the adapter placement search space, including long-range and recurrent adapters, which go beyond traditional uniform placement. This provides more flexibility and improves fine-tuning results. 2. The proposed gradient rank metric offers a practical and computationally efficient method for predicting the best adapter placements a priori, significantly reducing the computational cost of fine-tuning large models. 3. The method shows that even with a
1. **Dataset Limitation**: While the paper demonstrates the method's effectiveness on several datasets, the reviewer is curious about its performance on larger and more diverse datasets, such as ImageNet. Testing on such datasets would better illustrate the method's scalability and generalization capabilities. 2. **Adapter Complexity**: Introducing long-range and recurrent adapters increases overall model complexity. However, the paper does not fully explore the trade-offs between this complexi
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Taxonomy
TopicsDomain Adaptation and Few-Shot Learning · Speech Recognition and Synthesis · Machine Learning and ELM
MethodsAdapter