Feature Likelihood Divergence: Evaluating Generative Models Holistically

Alapfogalmak

Feature Likelihood Divergence (FLD) offers a comprehensive evaluation of generative models, addressing fidelity, diversity, and novelty.

Kivonat

The development of deep generative models has seen significant progress in producing high-dimensional, complex, and photo-realistic data. However, evaluating these models remains challenging due to limitations in standard likelihood-based metrics and sample-based metrics like FID. The proposed FLD metric aims to provide a trichotomic evaluation considering novelty, fidelity, and diversity of generated samples. By mapping samples to a feature space and using a mixture of Gaussians for density estimation, FLD can identify overfitting issues that other metrics may miss. The ability of FLD to capture sample fidelity, diversity, and novelty makes it a valuable tool for assessing generative models across various datasets and model classes.

Statisztikák

Standard likelihood-based metrics do not always correlate with perceptual fidelity.
Sample-based metrics like FID may overlook overfitting issues.
FLD provides a trichotomic evaluation considering novelty, fidelity, and diversity.
Mapping samples to a feature space is crucial for the computation of FLD.
Using a mixture of Gaussians helps estimate the density of generated samples.

Idézetek

"FLD enjoys the same scalability as popular sample-based metrics such as FID and IS but crucially also assesses sample novelty, overfitting, and memorization."
"We empirically demonstrate both on synthetic and real-world datasets that FLD can diagnose important failure modes such as memorization/overfitting."

Főbb Kivonatok

Feature Likelihood Divergence

by Marco Jirale... : arxiv.org 03-14-2024

https://arxiv.org/pdf/2302.04440.pdf

Mélyebb kérdések

How does the Feature Likelihood Divergence metric compare to other existing evaluation metrics for generative models

The Feature Likelihood Divergence (FLD) metric offers a comprehensive evaluation of generative models by considering fidelity, diversity, and novelty in the generated samples. In comparison to other existing metrics like Inception Score (IS) and Fréchet Inception Distance (FID), FLD provides a more nuanced assessment of sample quality. While IS and FID focus on perceptual quality and distribution similarity between generated and real data, FLD goes further to evaluate generalization capabilities by detecting overfitting through memorization detection.

What are the potential implications of identifying overfitting in deep generative models using tools like FLD

Identifying overfitting in deep generative models using tools like FLD can have significant implications for model performance and reliability. By pinpointing instances of overfitting where the model memorizes training data rather than generating novel samples, practitioners can address privacy concerns related to data leakage. This insight helps in ensuring that generative models maintain their ability to generalize beyond the training set, which is crucial for applications requiring diverse and authentic synthetic data.

How might the insights provided by FLD impact the future development and application of generative models

The insights provided by FLD could greatly impact the future development and application of generative models. By offering a holistic evaluation that considers fidelity, diversity, novelty, as well as identifying overfitting behaviors such as memorization, FLD guides researchers towards creating more robust and reliable generative models. This can lead to advancements in areas such as image synthesis, text generation, or audio processing where high-quality synthetic data is essential for various applications including content creation or data augmentation techniques. Additionally, understanding the limitations of current metrics through tools like FLD can drive innovation towards developing more efficient and effective evaluation protocols for evaluating complex generative models across different domains.