Three New Validators and a Large-Scale Benchmark Ranking for Unsupervised Domain Adaptation (2208.07360v4)

Abstract: Changes to hyperparameters can have a dramatic effect on model accuracy. Thus, the tuning of hyperparameters plays an important role in optimizing machine-learning models. An integral part of the hyperparameter-tuning process is the evaluation of model checkpoints, which is done through the use of "validators". In a supervised setting, these validators evaluate checkpoints by computing accuracy on a validation set that has labels. In contrast, in an unsupervised setting, the validation set has no such labels. Without any labels, it is impossible to compute accuracy, so validators must estimate accuracy instead. But what is the best approach to estimating accuracy? In this paper, we consider this question in the context of unsupervised domain adaptation (UDA). Specifically, we propose three new validators, and we compare and rank them against five other existing validators, on a large dataset of 1,000,000 checkpoints. Extensive experimental results show that two of our proposed validators achieve state-of-the-art performance in various settings. Finally, we find that in many cases, the state-of-the-art is obtained by a simple baseline method. To the best of our knowledge, this is the largest empirical study of UDA validators to date. Code is available at https://www.github.com/KevinMusgrave/powerful-benchmarker.

• The paper introduces three validators (BNM, ClassAMI, DEVN) that improve model performance estimation without relying on target labels.
• The study evaluates these validators on 31 UDA tasks using 1,000,000 checkpoints and weighted Spearman rank correlation to assess checkpoint ranking.
• Results show ClassAMI outperforms other methods in aligning validation scores with true model accuracy, highlighting the need for robust validator design.

Three New Validators and a Large-Scale Benchmark Ranking for Unsupervised Domain Adaptation

The exploration of validators in the context of unsupervised domain adaptation (UDA) is an area of increasing interest, particularly due to the critical role they play in hyperparameter tuning for models that rely on unlabelled data. This paper presents a comprehensive paper that introduces three new validators to the academic community and evaluates their performance against five established methods using an extensive dataset that includes 1,000,000 checkpoints across various UDA tasks.

Key Contributions

1. Validators in UDA Context: The paper identifies the essential function of validators in estimating model performance where direct measurement is not possible due to the absence of labeled target data. Recognizing a gap in the current research where most UDA studies focus on algorithms while relying on overly simplistic or impractical validators, it challenges prior conventions especially the reliance on oracle validators which depend on target-domain labels – a violation of the UDA setting.
2. Introduction of New Validators: The research introduces three new validators - Batch Nuclear-norm Maximization (BNM), ClassAMI, and a normalized variation of the Deep Embedded Validation (DEVN). BNM evaluates the prediction confidence diversity through singular value decomposition, ClassAMI uses mutual information to compare clustering quality, and DEVN adjusts the original DEV method by mitigating the impact of outliers through weight normalization.
3. Extensive Benchmark Dataset: The paper utilizes a dataset built from checkpoints produced using varied hyperparameters across several tasks. This includes 31 UDA tasks from popular datasets such as Office31, OfficeHome, and DomainNet126. The dataset serves as a robust foundation for evaluating validator performance through rank correlation metrics.
4. Performance Evaluation: The performance of validators is assessed by their ability to rank checkpoints relative to their actual accuracy, utilizing weighted Spearman correlation to reflect the selection’s impact on final model performance. This metric accounts for scenarios where the highest ranked model (based on validator score) does not necessarily have high accuracy, providing a more nuanced view of validator efficacy.

Results and Analysis

Key findings indicate that ClassAMI leads among new validators by successfully correlating validation scores with true model accuracy, outperforming several established validators. The paper highlights that:

• ClassAMI excels in delivering high correlation scores, particularly in settings where source validation accuracy was less useful.
• DEVN shows improved stability over its predecessor by consistent normalization practices.
• The baseline method of source validation accuracy still proves competitive and sometimes superior, demonstrating robustness but also highlighting the untapped potential of more sophisticated validators.

Interestingly, SND (Soft Neighborhood Density) underperformed persistently, especially in cases without distinct clustering. Furthermore, though the oracle validator serves as an upper bound for performance evaluations, it is infeasible for real-world application within true UDA contexts, underscoring the need for ongoing validator development.

Theoretical and Practical Implications

The findings of this paper lay groundwork for future improvements in UDA validator development, emphasizing a need to balance advancement in validator sophistication with practical applicability. While SOTA validators can enhance UDA effectiveness, poor performance or misalignment can nullify potential gains from UDA algorithms. Therefore, convergence towards better validators can unlock full utility from UDA models in real-world applications.

Future Directions

Further research could focus on exploring theoretical frameworks for validator design that inherently account for the peculiarities and complexities of UDA metrics. The leverage of more complex evaluation measures or the introduction of new machine learning paradigms could also provide insights leading to the design of novel and more efficient validators.

The presented empirical benchmark sets a reference point for future research in the field of UDA, establishing ClassAMI and related advancements as leading solutions for the challenges identified in machine learning validation without labeled data dependency.