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Unifying and extending Precision Recall metrics for assessing generative models (2405.01611v1)

Published 2 May 2024 in cs.LG, cs.AI, stat.ME, and stat.ML

Abstract: With the recent success of generative models in image and text, the evaluation of generative models has gained a lot of attention. Whereas most generative models are compared in terms of scalar values such as Frechet Inception Distance (FID) or Inception Score (IS), in the last years (Sajjadi et al., 2018) proposed a definition of precision-recall curve to characterize the closeness of two distributions. Since then, various approaches to precision and recall have seen the light (Kynkaanniemi et al., 2019; Naeem et al., 2020; Park & Kim, 2023). They center their attention on the extreme values of precision and recall, but apart from this fact, their ties are elusive. In this paper, we unify most of these approaches under the same umbrella, relying on the work of (Simon et al., 2019). Doing so, we were able not only to recover entire curves, but also to expose the sources of the accounted pitfalls of the concerned metrics. We also provide consistency results that go well beyond the ones presented in the corresponding literature. Last, we study the different behaviors of the curves obtained experimentally.

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Authors (3)
  1. Benjamin Sykes (1 paper)
  2. Loic Simon (14 papers)
  3. Julien Rabin (15 papers)
Citations (1)
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Summary

Unifying Precision and Recall Metrics for Generative Model Assessment

Overview

Assessing generative models, particularly those designed to produce realistic images or texts, is crucial yet challenging. Traditionally, metrics like FID (Fréchet Inception Distance) and IS (Inception Score) have been used. These metrics, however, only provide scalar values and don't capture the detailed discrepancy between the generated distribution (Q) and the real distribution (P), which could be essential in evaluating model performance comprehensively.

A more granular approach involves using precision-recall (PR) curves, a method that evaluates how good Q is at approximating P. Benjamin Sykes and colleagues present a unified approach to derive comprehensive PR curves by borrowing techniques from binary classification theories, which not only measure the extremities of data distributions but can detail the data distribution disparities across every threshold.

Precision-Recall Curves: Basics and Extension

Precision and recall in the context of generative models focus on capturing two facets:

  • Fidelity: How close the generated data points (from Q) are to the real data points (from P).
  • Diversity: How diverse the generated samples are compared to the variety present in P.

The PR curve considers each point in terms of these metrics, expressing a trade-off between fidelity (precision) and diversity (recall). The original utility of PR curves has been limited to discrete distributions, but this paper builds on previous works to adapt it for continuous cases.

Theoretical Unification of Metrics

Several recent studies proposed adjustments and alternatives to PR metrics, emphasizing extreme values of precision and recall, often focusing on minimal and maximal theoretical values. This paper instead argues for a more holistic view by charting the entire PR trajectory, calculated via empirical estimators derived from binary classification methods.

For instance, the paper discusses:

  • Improved Precision-Recall (IPR): Measures how often elements from Q appear within the nearest neighbors in P.
  • Coverage: Evaluates the fraction of elements from P that are nearest neighbors of samples from Q.

Methodological Enhancements and Consistency

The authors propose methodological improvements, such as data split (dividing data into training and validation sets for unbiased estimation) and adjusting neighborhood sizes in nearest neighbors methods (growing with the size of data points). These adjustments address biases and consistency issues in previous approaches, offering a potentially more robust estimation of PR curves.

Empirical Evaluation

The paper underscores the importance of comprehensive empirical testing. The authors contrast their unified model with former approaches across different scenarios: shifted distributions, cases with outliers, and various sample sizes. These experiments illustrate how previous methods might offer misleading or incomplete interpretations due to their narrow focus on extreme values alone.

Implications and Future Directions

The unified approach enables a detailed analysis of how generative models recreate and miss certain features of the data distribution. These insights could be critical for developing more nuanced generation techniques or tweaking existing models for specific applications.

Future research could explore automated ways of setting hyperparameters like the size of neighborhoods in nearest neighbor methods and investigate other forms of data distributions beyond images and texts. This continued refinement and understanding could drive more precise and comprehensive generative model development and evaluation.

In summary, while the quest for perfect generative models is far from over, this unified approach to precision and recall provides a more complete toolset for evaluating how well these models perform, setting the stage for more informed improvements in AI-driven generation technologies.