Fast yet Safe: Early-Exiting with Risk Control

Summary

This paper investigate how to adapt frameworks of risk control to early-exit neural networks (EENN). The new framework is designed to exit without severely degrading performance as well as to guarantee the reliability of the prediction during early exiting. The applied risk control technique offers a distribution-free, post-hoc solution that tunes the EENN’s exiting mechanism so that exits only occur when the output is of sufficient quality. Experiments on a series of tasks, including image classification, semantic segmentation, language modelling, and image generation show the effectiveness of the proposed method. -------------after rebuttal---------- The authors address most of my concerns. Although the single exiting threshold seems somehow a little flaw, the proposed new method is interesting and new.

Strengths

1. The experiments on different vision tasks can be strong evidence to show the effectiveness of the proposed method. 2. The theoretical analysis seems reasonable to be.

Weaknesses

The main weakness of this paper is under the strong assumption that all exiting thresholds are set to the same values. I admit that relaxing this assumption by adopting RC techniques for a high-dimensional threshold can lead to some difficulties in theoretical analysis, while using the same threshold for all exits may harm the overall performance of EENN. Only empirical test risk is used in all experiments. The general performance of EENN models in different tasks is also an important evaluation metric for EENN. The applicability of the proposed RC method can be impeded if the prediction performance is unsatisfactory when using the same exiting threshold for every exit classifiers, even though it has a lower risk in the prediction. It is also suggested to review the related important works in early exiting neural network areas, such as: [1] Improved techniques for training adaptive deep networks. in ICCV, 2019. [2] Resolution Adaptive Networks for Efficient Inference, in CVPR, 2020. [3] Learning to Weight Samples for Dynamic Early-exiting Networks, in ECCV, 2022.

Questions

See weakness. I think more evaluation metrics, such as test accuracy for classification tasks, or the IOU for segmentation tasks, should be included to show the effectiveness of the proposed method.

Rating

6

Confidence

3

Soundness

3

Presentation

3

Contribution

3

Limitations

See weakness. I think it is acceptable to use the same threshold for theoretical analysis. However, the real performance of the evaluated EENNs should also be attached in the manuscript.

Summary

This paper introduces a risk control framework for early-exit neural networks to balance the trade-off between inference efficiency and model performance. Compared to the conventional methods that manually set the confidence thresholds for early exiting, this work proposes a method to determine exit points from the perspective of risk control. Experiments on vision and language tasks demonstrate the effectiveness.

Strengths

* Novelty: The integration of risk control into EENNs is novel, and the studied problem is an interesting and important challenge in deploying early-exiting models; * Empirical Validation: The paper provides extensive experimental evidence across multiple domains, demonstrating the practical applicability of the proposed methods; * Soundness: The risk control framework is well-developed, offering both theoretical foundations and practical algorithms for determining exit thresholds.

Weaknesses

Overall I like the paper. A small suggestion is to demonstrate the accuracy-FLOPs curves as in the compared methods (MSDNet, L2W-DEN, and Dyn-Perceiver), because the current x-axis, "risk level", might be insufficiently straightforward.

Questions

Please see weaknesses.

Rating

6

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

yes

Summary

This paper proposes a method for improving the efficiency of early-exit neural networks (EENNs) while maintaining performance. ​ EENNs allow for predictions to be made at intermediate layers, resulting in faster inference. ​ However, the challenge is determining when it is safe to exit without sacrificing performance. ​ The authors address this issue by adapting risk control frameworks to EENNs. ​ They propose risk functions to measure the performance drop resulting from early-exiting and demonstrate the effectiveness of their approach on various vision and language tasks. ​

Strengths

- The paper addresses an important problem in machine learning - improving the efficiency of inference without sacrificing performance. ​ - The authors propose a novel approach of adapting risk control frameworks to EENNs, which provides a post-hoc solution for determining when it is safe to exit. ​ - The paper provides empirical validation of their approach on a range of vision and language tasks, demonstrating substantial computational savings while preserving performance goals. ​ - The authors consider both prediction quality and uncertainty estimation in their risk control framework, which is important for safety-critical scenarios. ​

Weaknesses

- The empirical validation of the approach is limited to a range of vision and language tasks. It would be beneficial to see the application of the method to other domains as well. - The paper does not discuss the limitations or potential drawbacks of the proposed approach. It would be helpful to have a discussion on the potential trade-offs or challenges in implementing the risk control framework in practice.

Questions

Refer to Weaknesses.

Rating

4

Confidence

3

Soundness

2

Presentation

2

Contribution

2

Limitations

N/A

Summary

This manuscript revisits the confidence-based thershold tuning for early-exit networks following a risk-control formulation. The proposed approach aims to provide an enhanced mechanism to identify confident predictions early-on during the computation, and exit early to improve inference efficiency with minimal effect on accuracy. The underlying trade-off between accuarcy and latency is balanced by a post-hoc solution, that determines the thershold value for the exits, through a risk control formulation considering a labelled calibration set (Performance Gap Risk), or the predictions of the final exit of the original model (Consistency Risk).

Strengths

- The manuscript studies a very interesting and timely problem, often overlook by the booming filed of early-exit models. - The proposed approach is post-hoc and can be applied to existing approaches without the need of re-training/finetunning the model, and does not add any computational overhead at inference time. - The effectiveness of the proposed approach is experimentally demonstrated on a wide range of predictive (image classification and segmentation) and generative tasks (language modelling and image generation), proving the generality of the proposed approach. - The paper is well-written and offers adequate background information and a nice formulation.

Weaknesses

- The reliance of the proposed approach to marginal monotonicity between performance and depth can be a limiting factor in some applications. E.g. recent works on EE-based LLMs indicate the predictive accuracy fluctuates notably across exits (see Elhoushi et al, LayerSkip, 2024 - Fig.2). - As stated in the limitations, the adoption of a single and static threshold across exits also significantly reduces the search space of the proposed approach. The inefficiency imposed by this design choice has not been quantified, but is likely to be significant without any calibratuon technics on exit confidence or special training of the model. - It is unclear how the proposed approach can be applied to the emerging line of works with learnable exit policies, as well as whether it will maintain its performance on different confidence metrics, such as state-saturation, used in LLMs etc

Questions

- Please consider the comments raised on the Weaknesses section and share any insights you may have on them. - Can the proposed Consistency Risk formulation be used for online adaptation of the thresholds, as during runtime calibration labels are not available, but input distribution shifts may occur? **Post-Rebuttal Edit:** Provisionally increasing my score from WA to A, given the thorough clarifications provided by the authors that adequately addressed most of my concerns.

Rating

7

Confidence

3

Soundness

3

Presentation

3

Contribution

3

Limitations

Limitations and future work are adequately discussed in the manuscript.