Everything about Inception score
Image algorithm
The Inception Score (IS) is an algorithm used to assess the quality of images created by a generative image model such as a generative adversarial network (GAN). The score is calculated based on the output of a separate, pretrained Inception v3 image classification model applied to a sample of (typically around 30,000) images generated by the generative model. The Inception Score is maximized when the following conditions are true:The entropy of the distribution of labels predicted by the Inceptionv3 model for the generated images is minimized. In other words, the classification model confidently predicts a single label for each image. Intuitively, this corresponds to the desideratum of generated images being "sharp" or "distinct". The predictions of the classification model are evenly distributed across all possible labels. This corresponds to the desideratum that the output of the generative model is "diverse". Wikipedia â
đ§ Start here: a 6-step reading path
- Read the overview: Inception scoreA 5-minute orientation
- Skim a recent paper: Methylprednisolone Pulses and Prolonged Remission in Systemic Lupus Erythematosus: A Propensity Score Analysis of the Longitudinal LupusâCrucesâBordeaux Inception CohortInfluential work since 2022
- Read the landmark paper: The SAMe-TT2R2 Score Predicts Poor Anticoagulation Control in AF Patients: A Prospective âReal-worldâ Inception Cohort StudyCited 51 times
- Go sideways: Generative adversarial networkA closely related topic
- Go sideways: Wasserstein GANA closely related topic
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đïž The subject at a glance
đ Landmark papers (most cited)
The SAMe-TT2R2 Score Predicts Poor Anticoagulation Control in AF Patients: A Prospective âReal-worldâ Inception Cohort Study
How multisystem inflammatory syndrome in children discriminated from Kawasaki disease: a differentiating score based on an inception cohort study
A simple score predicts future cardiovascular events in an inception cohort of dialysis patients
Quantum inception score
Analysis the structural complexity of âTimeâ in Hans Zimmerâs score for âInceptionâ
1762: From inception to the present: A review analysing the application of the radiomics quality score.
Mo1638 CROHNâS DISEASE POLYGENIC RISK SCORE IS ASSOCIATED WITH IBD-SEROLOGIES IN PEDIATRIC CROHNâS DISEASE INCEPTION COHORT
Mo1638 CROHNâS DISEASE POLYGENIC RISK SCORE IS ASSOCIATED WITH IBD-SEROLOGIES IN PEDIATRIC CROHNâS DISEASE INCEPTION COHORT
đ Influential since 2022
Methylprednisolone Pulses and Prolonged Remission in Systemic Lupus Erythematosus: A Propensity Score Analysis of the Longitudinal LupusâCrucesâBordeaux Inception Cohort
AB1419 IS DECREASED BODY MASS INDEX -2 Z SCORE OR LESS CORRELATING WITH AN ORGAN INVOLVEMENT PATTERN? RESULTS FROM THE JUVENILE SCLERODERMA INCEPTION COHORT
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đ Related topics
- Generative adversarial network A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. The concept wasâŠ
- Wasserstein GAN The Wasserstein Generative Adversarial Network (WGAN) is a variant of generative adversarial network (GAN) proposed in 2017 that aims to "improve the stabilityâŠ
- Generative model Generative models are a class of computational models frequently used for classification. In machine learning, it typically models the joint distributionâŠ
- Discriminative model Discriminative models, also referred to as conditional models, are a class of models frequently used for classification. In machine learning, it typicallyâŠ
- KullbackâLeibler divergence In mathematical statistics, the KullbackâLeibler (KL) divergence (also called relative entropy and I-divergence), denoted DKL(Pâ„Q), is a type of statisticalâŠ
- FrĂ©chet inception distance The FrĂ©chet inception distance (FID) is a metric used to assess the quality of images created by a generative model, like a generative adversarial networkâŠ
- Cross-entropy In information theory, the cross-entropy between two probability distributions p and q, over the same underlying set of events, measures the average numberâŠ
- Variational autoencoder In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling in 2013âŠ