Sampling-Based Testing for Accurate and Cost-Effective Operational Assessment of Deep Neural Networks

Core Concepts

Sampling-based techniques can provide unbiased, high-confidence estimates of deep neural network operational accuracy at low cost, while also exposing many mispredictions to support model improvement.

Abstract

The paper presents DeepSample, a family of sampling-based techniques for assessing the operational accuracy of deep neural networks (DNNs). The key insights are:

Sampling-based techniques can effectively estimate DNN operational accuracy by leveraging auxiliary variables (e.g., confidence, surprise) that are correlated with the failure probability.

The techniques differ in the sampling algorithm used (e.g., simple random sampling, unequal probability sampling, stratified sampling) and the auxiliary variables employed.

The techniques aim to achieve a threefold objective: build a small test dataset, provide unbiased and high-confidence accuracy estimates, and expose many mispredictions to support model improvement.

The authors implement five new DeepSample techniques and compare them with three existing state-of-the-art techniques on classification and regression tasks across multiple datasets and DNN models.

The results show that the new DeepSample techniques generally outperform the existing ones in terms of accuracy estimation and failure exposure, with the choice of sampling algorithm and auxiliary variables playing a key role.

The findings provide guidance for practitioners and researchers on effective use of sampling-based testing for DNN operational accuracy assessment.

Stats

The paper reports the following key figures:

The operational datasets have 60,500 examples for MNIST, 33,500 for CIFAR10, and 15,000 for CIFAR100.
The DNN models have between 6 to 16 layers and 97,114 to 15,047,588 parameters.
The DNN model accuracies range from 57.4% to 94.8% on the classification tasks, and 0.904 to 0.918 on the regression tasks.

Quotes

"The challenge is to build a small test set able to provide an unbiased, high-confidence estimate of the DNN accuracy. At the same time, testers are interested in exposing DNN mispredictions, since they are input to DNN debugging and re-training."
"The goal thus becomes threefold: build a small dataset, able to faithfully estimate DNN accuracy, and with a good ability to expose mispredictions."

Key Insights Distilled From

DeepSample

by Antonio Guer... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19271.pdf

Deeper Inquiries

What other types of auxiliary variables beyond confidence, surprise, and autoencoder reconstruction error could be leveraged to further improve the sampling-based testing techniques

In addition to confidence, surprise, and autoencoder reconstruction error, other types of auxiliary variables that could be leveraged to further improve sampling-based testing techniques include:

Feature Importance: By analyzing the importance of different features in the input data for the DNN model, we can identify which features have a significant impact on the model's predictions. Sampling based on these important features can help expose mispredictions more effectively.

Data Drift Metrics: Incorporating metrics that measure data drift, such as distribution shifts or changes in data patterns over time, can help in adapting the sampling strategy to handle concept drift in the operational data.

Model Uncertainty: Utilizing measures of uncertainty in the model predictions, such as entropy or variance in prediction probabilities, can guide the sampling process towards examples where the model is uncertain, potentially leading to more informative test cases.

Data Complexity: Considering the complexity of the input data, such as the presence of outliers, noise, or rare instances, can help in selecting test cases that challenge the model's generalization capabilities.

Temporal Information: Incorporating temporal information, such as timestamps or sequential patterns in the data, can enable the sampling techniques to adapt to changes over time and provide continuous accuracy assessment.

How can the sampling-based techniques be extended to handle concept drift in the operational data over time, and provide continuous, adaptive accuracy assessment

To handle concept drift in the operational data over time and provide continuous, adaptive accuracy assessment, the sampling-based techniques can be extended in the following ways:

Dynamic Sampling Strategies: Implement adaptive sampling strategies that continuously monitor the performance of the DNN model and adjust the sampling process based on the evolving data distribution. This can involve re-evaluating the auxiliary variables, updating partitioning strategies, and modifying the sampling probabilities in real-time.

Incremental Learning: Integrate incremental learning techniques that allow the model to adapt to new data instances and update its parameters gradually. By incorporating new data into the training process, the model can stay up-to-date with changing patterns in the operational data.

Ensemble Methods: Employ ensemble methods that combine multiple DNN models trained on different subsets of the data. By aggregating predictions from diverse models, the ensemble can provide more robust and stable accuracy estimates, even in the presence of concept drift.

Feedback Mechanisms: Implement feedback loops that capture the model's performance on new data samples and use this feedback to adjust the sampling strategy. This continuous feedback mechanism can help in detecting and responding to concept drift effectively.

Monitoring and Alerting: Develop monitoring systems that track key performance metrics, detect deviations from expected behavior, and trigger alerts when significant concept drift is detected. This proactive approach can enable timely intervention and re-evaluation of the model's accuracy.

How can the insights from this work on sampling-based DNN testing be applied to improve the testing and monitoring of other types of machine learning models deployed in software systems

The insights from sampling-based DNN testing can be applied to improve the testing and monitoring of other types of machine learning models deployed in software systems in the following ways:

Model Evaluation: Utilize sampling techniques to assess the operational accuracy of various machine learning models, including decision trees, support vector machines, and ensemble models. By selecting representative test cases and exposing mispredictions, the overall model performance can be evaluated effectively.

Anomaly Detection: Apply sampling-based testing to detect anomalies and outliers in anomaly detection models, such as isolation forests or one-class SVMs. By sampling data points that challenge the anomaly detection capabilities, the robustness and reliability of the models can be evaluated.

Reinforcement Learning: Extend sampling strategies to evaluate the performance of reinforcement learning algorithms in dynamic environments. By selecting diverse and challenging scenarios for the reinforcement learning agent, the effectiveness and adaptability of the learning process can be assessed.

Natural Language Processing: Implement sampling techniques to test the accuracy and robustness of natural language processing models, such as sentiment analysis or text classification algorithms. By selecting a diverse set of text samples, the models' performance in handling different linguistic patterns and contexts can be evaluated.

Time Series Forecasting: Apply sampling-based testing to assess the accuracy of time series forecasting models, including ARIMA, LSTM, or Prophet models. By selecting time series data points that represent different trends and patterns, the models' predictive capabilities can be evaluated under varying conditions.