Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation | Paper Citation Record

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This paper cites GPT-4 Technical Report.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation GPT-4 Technical Report

Reference 1

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This paper cites Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Reference 2

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This paper cites AI-generated faces influence gender stereotypes and racial homogenization.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation AI-generated faces influence gender stereotypes and racial homogenization

Reference 3

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This paper cites Detecting Out- Of-Distribution Earth Observation Images with Diffusion Models.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Detecting Out- Of-Distribution Earth Observation Images with Diffusion Models

Reference 4

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This paper cites Shedding light on large generative networks: Estimating epistemic uncertainty in diffusion models.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Shedding light on large generative networks: Estimating epistemic uncertainty in diffusion models

Reference 5

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This paper cites Estimating Epistemic and Aleatoric Uncertainty with a Single Model.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Estimating Epistemic and Aleatoric Uncertainty with a Single Model

Reference 6

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This paper cites PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation PixArt-\Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

Reference 7

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This paper cites Pixart-$ \alpha$: Fast train- ing of diffusion transformer for photorealistic text-to-image synthesis.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Pixart-$ \alpha$: Fast train- ing of diffusion transformer for photorealistic text-to-image synthesis

Reference 8

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This paper cites Cimpoi, S.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Cimpoi, S

Reference 9

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This paper cites Addressing failure prediction by learning model confidence.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Addressing failure prediction by learning model confidence

Reference 10

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This paper cites Imagenet: A large-scale hierarchical image database.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Imagenet: A large-scale hierarchical image database

Reference 12

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This paper cites Gpt4image: Can large pre-trained models help vision models on perception tasks? arXiv e-prints , pages arXiv–2306, 2023.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Gpt4image: Can large pre-trained models help vision models on perception tasks? arXiv e-prints , pages arXiv–2306, 2023

Reference 13

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This paper cites Diffusion-based probabilis- tic uncertainty estimation for active domain adaptation.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Diffusion-based probabilis- tic uncertainty estimation for active domain adaptation

Reference 14

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This paper cites The Llama 3 Herd of Models.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation The Llama 3 Herd of Models

Reference 15

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Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Masksembles for uncertainty estimation

Reference 16

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This paper cites Taming transformers for high-resolution image synthesis.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Taming transformers for high-resolution image synthesis

Reference 17

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This paper cites Scaling rec- tified flow transformers for high-resolution image synthesis.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Scaling rec- tified flow transformers for high-resolution image synthesis

Reference 18

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This paper cites TRADI: Tracking deep neural network weight distributions.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation TRADI: Tracking deep neural network weight distributions

Reference 19

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This paper cites Latent discriminant deterministic uncertainty.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Latent discriminant deterministic uncertainty

Reference 20

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This paper cites Uncertainty in Deep Learning.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Uncertainty in Deep Learning

Reference 21

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This paper cites Selective classification for deep neural networks.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Selective classification for deep neural networks

Reference 22

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This paper cites Denoising diffusion models for out-of-distribution detection.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Denoising diffusion models for out-of-distribution detection

Reference 23

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This paper cites A baseline for de- tecting misclassified and out-of-distribution examples in neural networks.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation A baseline for de- tecting misclassified and out-of-distribution examples in neural networks

Reference 24

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This paper cites Denoising dif- fusion probabilistic models.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Denoising dif- fusion probabilistic models

Reference 25

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This paper cites Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

Reference 26

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This paper cites What are bayesian neural network posteriors really like? In ICML, 2021.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation What are bayesian neural network posteriors really like? In ICML, 2021

Reference 27

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This paper cites Highly accurate protein structure prediction with alphafold.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Highly accurate protein structure prediction with alphafold

Reference 28

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This paper cites Elucidating the design space of diffusion-based generative models.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Elucidating the design space of diffusion-based generative models

Reference 29

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This paper cites What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30, 2017

Reference 30

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This paper cites Variational diffusion models.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Variational diffusion models

Reference 31

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This paper cites Semantic uncertainty: Linguistic invariances for uncertainty estima- tion in natural language generation.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Semantic uncertainty: Linguistic invariances for uncertainty estima- tion in natural language generation

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This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 33

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This paper cites Learning skillful medium-range global weather forecasting.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Learning skillful medium-range global weather forecasting

Reference 34

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This paper cites Packed-ensembles for efficient uncertainty estimation.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Packed-ensembles for efficient uncertainty estimation

Reference 35

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This paper cites LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Reference 36

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This paper cites Rouge: A package for automatic evaluation of summaries.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Rouge: A package for automatic evaluation of summaries

Reference 37

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This paper cites Detecting the unexpected via image resynthesis.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Detecting the unexpected via image resynthesis

Reference 38

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This paper cites Unsupervised out-of-distribution detection with diffusion inpainting.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Unsupervised out-of-distribution detection with diffusion inpainting

Reference 39

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Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Dpm-solver: A fast ode solver for diffu- sion probabilistic model sampling in around 10 steps

Reference 40

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Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Predictive uncertainty es- timation via prior networks

Reference 41

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Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Mcmc using hamiltonian dynamics

Reference 42

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Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Bleu: a method for automatic evaluation of machine translation

Reference 43

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This paper cites Nuclei segmentation in micro- scope cell images.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Nuclei segmentation in micro- scope cell images

Reference 44

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Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Understand- ing softmax confidence and uncertainty, 2021

Reference 45

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This paper cites Scalable diffusion models with transformers.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Scalable diffusion models with transformers

Reference 46

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Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Bigearthnet

Reference 47

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This paper cites Sdxl: Improving latent diffusion models for high-resolution image synthesis.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Sdxl: Improving latent diffusion models for high-resolution image synthesis

Reference 48

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This paper cites Zero-shot text-to-image generation.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Zero-shot text-to-image generation

Reference 49

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This paper cites High-resolution image synthesis with latent diffusion models.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation High-resolution image synthesis with latent diffusion models

Reference 50

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This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 51

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This paper cites Denois- ing diffusion implicit models.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Denois- ing diffusion implicit models

Reference 52

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This paper cites Score- based generative modeling through stochastic differential equations.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Score- based generative modeling through stochastic differential equations

Reference 53

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This paper cites Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models

Reference 54

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This paper cites Road anomaly detection by partial image reconstruction with segmentation coupling.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Road anomaly detection by partial image reconstruction with segmentation coupling

Reference 55

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Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Bayesian learning via stochastic gradient langevin dynamics

Reference 56

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This paper cites BatchEnsemble: an alternative approach to efficient ensemble and lifelong learning.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation BatchEnsemble: an alternative approach to efficient ensemble and lifelong learning

Reference 57

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This paper cites Augmenting softmax information for selective classification with out-of- distribution data.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Augmenting softmax information for selective classification with out-of- distribution data

Reference 58

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This paper cites On the usefulness of deep ensemble diversity for out-of-distribution detection, 2022.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation On the usefulness of deep ensemble diversity for out-of-distribution detection, 2022

Reference 59

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This paper cites Window- based early-exit cascades for uncertainty estimation: When deep ensembles are more efficient than single models.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Window- based early-exit cascades for uncertainty estimation: When deep ensembles are more efficient than single models

Reference 60

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This paper cites Score Normalization for a Faster Diffusion Exponential Integrator Sampler.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Score Normalization for a Faster Diffusion Exponential Integrator Sampler

Reference 61

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This paper cites Towards understanding why label smooth- ing degrades selective classification and how to fix it, 2024.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Towards understanding why label smooth- ing degrades selective classification and how to fix it, 2024

Reference 62

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This paper cites Synthesize then compare: Detecting failures and anomalies for semantic segmentation.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Synthesize then compare: Detecting failures and anomalies for semantic segmentation

Reference 63

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Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Jaakkola

Reference 64

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This paper cites Generalized out-of-distribution detection: A survey.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Generalized out-of-distribution detection: A survey

Reference 65

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This paper cites Sdxs: Real-time one-step latent diffusion models with image conditions.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Sdxs: Real-time one-step latent diffusion models with image conditions

Reference 66

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This paper cites Fast sampling of dif- fusion models with exponential integrator.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Fast sampling of dif- fusion models with exponential integrator

Reference 67

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This paper cites The unreasonable effectiveness of deep features as a perceptual metric.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation The unreasonable effectiveness of deep features as a perceptual metric

Reference 68

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Observation a32acfe9-357d-408d-8c00-30eff4bd2e0a · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation BERTScore: Evaluating Text Generation with BERT

Reference 69

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This paper cites To generate or not? safety-driven unlearned diffusion models are still easy to generate unsafe images.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation To generate or not? safety-driven unlearned diffusion models are still easy to generate unsafe images

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:45:19.744729Z

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This paper cites These prompts create an ambiguous context, simulating scenarios where the input information is too sparse for the model to fully comprehend.

Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation These prompts create an ambiguous context, simulating scenarios where the input information is too sparse for the model to fully comprehend

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:45:19.606586Z

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Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Unresolved cited work

Reference 72

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Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation yes” or “no

Reference 73

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Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation Unresolved cited work

Reference 74

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