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A practical generalization metric for deep networks benchmarking

Mengqing Huang, Hongchuan Yu, Jianjun Zhang

TL;DR

A practical generalization metric is introduced for benchmarking different deep networks and a novel testbed is proposed for the verification of theoretical estimations, indicating that a deep network’s generalization capacity in classification tasks is contingent upon both classification accuracy and the diversity of unseen data.

Abstract

There is an ongoing and dedicated effort to estimate bounds on the generalization error of deep learning models, coupled with an increasing interest with practical metrics that can be used to experimentally evaluate a model's ability to generalize. This interest is not only driven by practical considerations but is also vital for theoretical research, as theoretical estimations require practical validation. However, there is currently a lack of research on benchmarking the generalization capacity of various deep networks and verifying these theoretical estimations. This paper aims to introduce a practical generalization metric for benchmarking different deep networks and proposes a novel testbed for the verification of theoretical estimations. Our findings indicate that a deep network's generalization capacity in classification tasks is contingent upon both classification accuracy and the diversity of unseen data. The proposed metric system is capable of quantifying the accuracy of deep learning models and the diversity of data, providing an intuitive and quantitative evaluation method, a trade-off point. Furthermore, we compare our practical metric with existing generalization theoretical estimations using our benchmarking testbed. It is discouraging to note that most of the available generalization estimations do not correlate with the practical measurements obtained using our proposed practical metric. On the other hand, this finding is significant as it exposes the shortcomings of theoretical estimations and inspires new exploration.

A practical generalization metric for deep networks benchmarking

TL;DR

A practical generalization metric is introduced for benchmarking different deep networks and a novel testbed is proposed for the verification of theoretical estimations, indicating that a deep network’s generalization capacity in classification tasks is contingent upon both classification accuracy and the diversity of unseen data.

Abstract

There is an ongoing and dedicated effort to estimate bounds on the generalization error of deep learning models, coupled with an increasing interest with practical metrics that can be used to experimentally evaluate a model's ability to generalize. This interest is not only driven by practical considerations but is also vital for theoretical research, as theoretical estimations require practical validation. However, there is currently a lack of research on benchmarking the generalization capacity of various deep networks and verifying these theoretical estimations. This paper aims to introduce a practical generalization metric for benchmarking different deep networks and proposes a novel testbed for the verification of theoretical estimations. Our findings indicate that a deep network's generalization capacity in classification tasks is contingent upon both classification accuracy and the diversity of unseen data. The proposed metric system is capable of quantifying the accuracy of deep learning models and the diversity of data, providing an intuitive and quantitative evaluation method, a trade-off point. Furthermore, we compare our practical metric with existing generalization theoretical estimations using our benchmarking testbed. It is discouraging to note that most of the available generalization estimations do not correlate with the practical measurements obtained using our proposed practical metric. On the other hand, this finding is significant as it exposes the shortcomings of theoretical estimations and inspires new exploration.
Paper Structure (10 sections, 7 equations, 3 figures, 3 tables)

This paper contains 10 sections, 7 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed practical generalization metric
  4. Benchmark Testbed
  5. Practical Generalization Metric
  6. Benchmarking Tests
  7. Data and Test Models
  8. Trade-Off points of CLIP and EfficientNet
  9. Consistency check with existing Generalisation Estimations
  10. Conclusions

Figures (3)

  • Figure 1: (a)Illustration of Benchmark Testbed; (b)A 3D array consists of cells $(g,k)$, and the pink piece refers to the slice without noise (SSIM=1) and blue piece refers to the slice with zero-shot%=0.
  • Figure 2: TradeOff points of two kinds models, CLIP and EfficientNet (denoted as $"\star"$). The solid vertical lines indicate the selection of trade-off points on each marginals. (a)-(c) CLIP on ImageNet, (d)-(f) EfficientNet on ImageNet, (g)-(i) CLIP on CIFAR-100, (j)-(l) EfficientNet on CIFAR-100
  • Figure 3: Upper row: Four marginal probabilities of two slices with respect to the dimension $WeightNum$: (a) CLIP (b) EfficientNet on ImageNet, (c) CLIP (d) EfficientNet on CIFAR-100. Bottom row: Scatter plots of the sign-errors: (e) related to SSIM on ImageNet, (f) related to ZeroShot on ImageNet, (g) related to SSIM on CIFAR-100, (h) related to ZeroShot on CIFAR-100.