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Feature Likelihood Score: Evaluating the Generalization of Generative Models Using Samples
Marco Jiralerspong · Joey Bose · Ian Gemp · Chongli Qin · Yoram Bachrach · Gauthier Gidel

Thu Dec 14 08:45 AM -- 10:45 AM (PST) @ Great Hall & Hall B1+B2 #602

The past few years have seen impressive progress in the development of deep generative models capable of producing high-dimensional, complex, and photo-realistic data. However, current methods for evaluating such models remain incomplete: standard likelihood-based metrics do not always apply and rarely correlate with perceptual fidelity, while sample-based metrics, such as FID, are insensitive to overfitting, i.e., inability to generalize beyond the training set. To address these limitations, we propose a new metric called the Feature Likelihood Score (FLS), a parametric sample-based score that uses density estimation to provide a comprehensive trichotomic evaluation accounting for novelty (i.e., different from the training samples), fidelity, and diversity of generated samples. We empirically demonstrate the ability of FLS to identify specific overfitting problem cases, where previously proposed metrics fail. We also extensively evaluate FLS on various image datasets and model classes, demonstrating its ability to match intuitions of previous metrics like FID while offering a more comprehensive evaluation of generative models.

Author Information

Marco Jiralerspong (Université de Montréal/Mila)
Joey Bose (McGill/Mila)

I’m a PhD student at the RLLab at McGill/MILA where I work on Adversarial Machine Learning on Graphs. Previously, I was a Master’s student at the University of Toronto where I researched crafting Adversarial Attacks on Computer Vision models using GAN’s. I also interned at Borealis AI where I was working on applying adversarial learning principles to learn better embeddings i.e. Word Embeddings for Machine Learning models.

Ian Gemp (DeepMind)
Chongli Qin (DeepMind)
Yoram Bachrach (Google DeepMind)
Gauthier Gidel (Mila)

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