# Evaluation Metrics for Conditional Image Generation

arXiv: https://arxiv.org/abs/2004.12361 · HTML: https://mlsanity.com/paper/2004.12361/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2020.

## Built on by (most cited first, 19 of 19)

- [Conditional Image-to-Video Generation with Latent Flow Diffusion Models](https://mlsanity.com/paper/2303.13744/) (2023)
- [Time Weaver: A Conditional Time Series Generation Model](https://mlsanity.com/paper/2403.02682/) (2024)
- [FlexIT: Towards Flexible Semantic Image Translation](https://mlsanity.com/paper/2203.04705/) (2022)
- [Diffusion Illusions: Hiding Images in Plain Sight](https://mlsanity.com/paper/2312.03817/) (2023)
- [Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design](https://mlsanity.com/paper/2302.02913/) (2023)
- [Image-Based Virtual Try-On: A Survey](https://mlsanity.com/paper/2311.04811/) (2023)
- [Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals](https://mlsanity.com/paper/2106.07898/) (2021)
- [Conditional Frechet Inception Distance](https://mlsanity.com/paper/2103.11521/) (2021)
- [Semantic Communications for Artificial Intelligence Generated Content (AIGC) Toward Effective Content Creation](https://mlsanity.com/paper/2308.04942/) (2023)
- [Elucidating the design space of language models for image generation](https://mlsanity.com/paper/2410.16257/) (2024)
- [Manifold-Aware Perturbations for Constrained Generative Modeling](https://mlsanity.com/paper/2601.23151/) (2026)
- [Generative Models with Information-Theoretic Protection Against Membership Inference Attacks](https://mlsanity.com/paper/2206.00071/) (2022)
- [Disentangling Mean Embeddings for Better Diagnostics of Image Generators](https://mlsanity.com/paper/2409.01314/) (2024)
- [Counterfactual Edits for Generative Evaluation](https://mlsanity.com/paper/2303.01555/) (2023)
- [DPAF: Image Synthesis via Differentially Private Aggregation in Forward Phase](https://mlsanity.com/paper/2304.12185/) (2023)
- [Conditional Idempotent Generative Networks](https://mlsanity.com/paper/2406.02841/) (2024)
- [Conformal Reliability: A New Evaluation Metric for Conditional Generation](https://mlsanity.com/paper/2605.30807/) (2026)
- [What FID Hides: Detecting, Ranking, and Diagnosing Deviations in Generative Evaluation](https://mlsanity.com/paper/2608.24881/) (2026)
- [MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation](https://mlsanity.com/paper/2610.17014/) (2026)

## Abstract (arXiv)

We present two new metrics for evaluating generative models in the class-conditional image generation setting. These metrics are obtained by generalizing the two most popular unconditional metrics: the Inception Score (IS) and the Fre'chet Inception Distance (FID). A theoretical analysis shows the motivation behind each proposed metric and links the novel metrics to their unconditional counterparts. The link takes the form of a product in the case of IS or an upper bound in the FID case. We provide an extensive empirical evaluation, comparing the metrics to their unconditional variants and to other metrics, and utilize them to analyze existing generative models, thus providing additional insights about their performance, from unlearned classes to mode collapse.