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A Note on the Inception Score

11 Pith papers cite this work. Polarity classification is still indexing.

11 Pith papers citing it
abstract

Deep generative models are powerful tools that have produced impressive results in recent years. These advances have been for the most part empirically driven, making it essential that we use high quality evaluation metrics. In this paper, we provide new insights into the Inception Score, a recently proposed and widely used evaluation metric for generative models, and demonstrate that it fails to provide useful guidance when comparing models. We discuss both suboptimalities of the metric itself and issues with its application. Finally, we call for researchers to be more systematic and careful when evaluating and comparing generative models, as the advancement of the field depends upon it.

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representative citing papers

Diffusion Models Beat GANs on Image Synthesis

cs.LG · 2021-05-11 · accept · novelty 7.0

Diffusion models with architecture improvements and classifier guidance achieve superior FID scores to GANs on unconditional and conditional ImageNet image synthesis.

Post-Training Pruning for Diffusion Transformers

cs.CV · 2026-07-01 · unverdicted · novelty 6.0

DiT-Pruning introduces an energy-based saliency metric balancing weights and activations plus clustering-aware granularity for post-training pruning of DiTs, showing near-zero CLIP score degradation at 50% sparsity on FLUX.1-dev.

Diffusion Fine-tuning with Rewarded Moment Matching Distillation

cs.LG · 2026-06-29 · unverdicted · novelty 6.0

RMMD simultaneously distills diffusion models and optimizes rewards, yielding better FID-reward trade-offs on ImageNet than DI++, DRaFT and HyperNoise, and a 7.5x faster GenCast model that beats its teacher on 93% of weather variables while improving calibration.

Generative Recursive Reasoning

cs.AI · 2026-05-19 · unverdicted · novelty 6.0 · 2 refs

GRAM is a latent-variable generative model that performs recursive reasoning via stochastic trajectories, trained with amortized variational inference to support multi-hypothesis reasoning and unconditional generation.

Movie Gen: A Cast of Media Foundation Models

cs.CV · 2024-10-17 · unverdicted · novelty 5.0

A 30B-parameter transformer and related models generate high-quality videos and audio, claiming state-of-the-art results on text-to-video, video editing, personalization, and audio generation tasks.

Image-to-Video Diffusion: From Foundations to Open Frontiers

cs.CV · 2026-05-17 · unverdicted · novelty 3.0

A survey that organizes diffusion image-to-video methods into a taxonomy, distills core designs in condition encoding, temporal modeling, noise prior, and upsampling, and discusses applications plus challenges.

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