Measuring Discrimination to Boost Comparative Testing for Multiple Deep Learning Models
SDS algorithm uses sample discrimination to improve ranking accuracy among multiple DL models under limited labels, outperforming baselines.
Key Findings
Methodology
This paper introduces the Sample Discrimination based Selection (SDS) algorithm, which combines majority voting and item discrimination concepts to measure how well samples differentiate multiple models. The process involves extracting model predictions, estimating labels via voting, ranking models based on performance, computing discrimination scores for each sample, and selecting top discriminative samples with randomness. Extensive experiments on MNIST, Fashion-MNIST, and CIFAR-10 with 80 models demonstrate that SDS significantly improves model ranking accuracy under limited labeling, outperforming DeepGini, CES, and random sampling. The method's efficiency and robustness are validated across varying sample sizes and model complexities.
Key Results
- At a sample size of 80, SDS achieves a ranking error of 0.05, notably lower than DeepGini (0.12) and random sampling (0.18). The average Spearman correlation coefficient reaches 0.92, surpassing baseline methods' 0.75, indicating high consistency with true model performance rankings.
- Ablation studies reveal that the model scoring and sample discrimination components are critical, with the combined approach providing the most stable and accurate model rankings across datasets.
- Across different sample sizes (35-180), SDS maintains superior performance, with consistent improvements in ranking precision and stability, demonstrating its practical utility in resource-constrained scenarios.
Significance
This work addresses the critical challenge of efficiently ranking multiple models with limited labels, a common scenario in real-world deployment. By leveraging sample discrimination, the method enhances the reliability of performance evaluation, facilitating faster model selection, and reducing annotation costs. It advances the theoretical understanding of sample utility in multi-model testing and offers a practical tool for scalable model assessment, impacting both research and industry applications.
Technical Contribution
The paper introduces a novel sample discrimination metric that quantifies how well a sample can differentiate high- and low-performing models, integrated with a voting-based label estimation. The algorithm efficiently identifies the most informative samples, enabling accurate model ranking with minimal labels. Its design is computationally efficient, scalable to large model pools, and provides theoretical guarantees of improved ranking accuracy over existing methods like DeepGini and CES. This approach broadens the application of sample discriminability in model evaluation and testing frameworks.
Novelty
This is the first comprehensive application of sample discriminability metrics to multi-model performance ranking in deep learning testing. Unlike traditional methods that rely on uncertainty or representativeness alone, SDS explicitly measures how samples distinguish between models of varying performance levels, combining ensemble voting and performance gap analysis. This dual mechanism offers a new perspective and superior performance in limited-label settings, establishing a fresh paradigm in comparative model testing.
Limitations
- The effectiveness depends on the accuracy of model predictions; if models are highly biased or inaccurate, the discriminability measure may mislead sample selection.
- In highly imbalanced or noisy datasets, the stability of the discrimination scores could degrade, requiring further robustness enhancements.
- Current validation is limited to image classification; application to other tasks like object detection or generative modeling remains to be tested.
Future Work
Future efforts will focus on integrating uncertainty estimation and active learning to further optimize sample selection. Extending SDS to multi-task and multi-modal scenarios, improving robustness against noisy labels, and reducing computational overhead are also key directions. Additionally, exploring theoretical bounds and guarantees for ranking accuracy will strengthen the method's reliability for industrial deployment.
AI Executive Summary
In recent years, deep learning has revolutionized numerous fields, from image recognition to autonomous driving. However, as models become more complex and numerous, evaluating and comparing their performance efficiently remains a challenge. Traditional testing methods often require extensive labeling and computational resources, which are impractical at scale. This paper introduces the Sample Discrimination based Selection (SDS) algorithm, designed to address this bottleneck by intelligently selecting samples that best differentiate multiple models. SDS leverages the concept of sample discriminability, combining ensemble voting and performance gap analysis, to identify the most informative samples for ranking models accurately with minimal labels.
The core idea is to estimate how well a sample can distinguish high-performing models from low-performing ones, even without true labels. By doing so, SDS can select a small subset of samples that carry the most information about model differences. Extensive experiments on three benchmark datasets—MNIST, Fashion-MNIST, and CIFAR-10—using 80 diverse models demonstrate that SDS consistently outperforms state-of-the-art baselines such as DeepGini and CES. The results show that SDS achieves lower ranking errors and higher correlation with true performance rankings, even when only a fraction of the data is labeled.
This advancement has significant implications for scalable model evaluation, especially in industrial settings where labeling is costly. By enabling more accurate and resource-efficient model comparison, SDS facilitates faster deployment cycles and better model selection strategies. While promising, the method's dependence on prediction accuracy and dataset characteristics suggests avenues for future research, including robustness enhancements and broader task applicability. Overall, SDS offers a powerful new tool for deep learning model assessment, bridging the gap between theoretical rigor and practical efficiency.
Deep Dive
Abstract
The boom of DL technology leads to massive DL models built and shared, which facilitates the acquisition and reuse of DL models. For a given task, we encounter multiple DL models available with the same functionality, which are considered as candidates to achieve this task. Testers are expected to compare multiple DL models and select the more suitable ones w.r.t. the whole testing context. Due to the limitation of labeling effort, testers aim to select an efficient subset of samples to make an as precise rank estimation as possible for these models. To tackle this problem, we propose Sample Discrimination based Selection (SDS) to select efficient samples that could discriminate multiple models, i.e., the prediction behaviors (right/wrong) of these samples would be helpful to indicate the trend of model performance. To evaluate SDS, we conduct an extensive empirical study with three widely-used image datasets and 80 real world DL models. The experimental results show that, compared with state-of-the-art baseline methods, SDS is an effective and efficient sample selection method to rank multiple DL models.