Paper Citation Record · LEDGER

A Simple and Efficient Baseline for Zero-Shot Generative Classification

As of 12 August 2026, this Paper Citation Record lists 75 of 75 outbound references and 2 inbound Pith citation observations for arXiv:2412.12594.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.12594 v1

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measured 75 of 75 reference resolution

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Reference resolution

75 of 75 outbound references displayed

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External citation measurements

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Outbound references

Observation 2787c472-9c5b-4860-b2c7-212e0f99e693 · outbound

This paper cites Zigzag diffusion sampling: The path to success is zigzag, 2024.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Zigzag diffusion sampling: The path to success is zigzag, 2024

Reference 1

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A Simple and Efficient Baseline for Zero-Shot Generative Classification Unresolved cited work

Reference 2

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This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 3

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This paper cites Food-101–mining discriminative components with random forests.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Food-101–mining discriminative components with random forests

Reference 4

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This paper cites Language models are few-shot learners.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Language models are few-shot learners

Reference 5

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Observation 4d0d83b5-79f9-46f2-8ffa-ed5ba684fba7 · outbound

This paper cites Gen- erating visual representations for zero-shot classification.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Gen- erating visual representations for zero-shot classification

Reference 6

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Observation 0eaece84-894e-49c7-9246-e40d1ed7925e · outbound

This paper cites Peekaboo: Text to Image Diffusion Models are Zero-Shot Segmentors.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Peekaboo: Text to Image Diffusion Models are Zero-Shot Segmentors

Reference 7

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This paper cites Robust Classification via a Single Diffusion Model.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Robust Classification via a Single Diffusion Model

Reference 8

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This paper cites Cimpoi, S.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Cimpoi, S

Reference 9

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Observation 8c9e3cd6-4657-4b3a-8b0c-e36cc95886b9 · outbound

This paper cites Text-to-image diffusion mod- els are zero-shot classifiers.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Text-to-image diffusion mod- els are zero-shot classifiers

Reference 10

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Observation 949d4467-ab47-4a9c-b16e-36e696cc2b36 · outbound

This paper cites An analysis of single-layer networks in unsupervised feature learning.

A Simple and Efficient Baseline for Zero-Shot Generative Classification An analysis of single-layer networks in unsupervised feature learning

Reference 11

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Observation c6fff904-94ef-4198-b761-c2f7c2d7f3c8 · outbound

This paper cites Gan- bert: Generative adversarial learning for robust text classifica- tion with a bunch of labeled examples.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Gan- bert: Generative adversarial learning for robust text classifica- tion with a bunch of labeled examples

Reference 12

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

A Simple and Efficient Baseline for Zero-Shot Generative Classification Imagenet: A large-scale hierarchical image database

Reference 13

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This paper cites Diffusion models beat gans on image synthesis.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Diffusion models beat gans on image synthesis

Reference 14

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This paper cites Class prior estimation from positive and unlabeled data.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Class prior estimation from positive and unlabeled data

Reference 15

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Observation e77fe65e-74c8-417d-be27-3deb15fca18f · outbound

This paper cites One-shot learn- ing of object categories.

A Simple and Efficient Baseline for Zero-Shot Generative Classification One-shot learn- ing of object categories

Reference 16

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This paper cites Initno: Boosting text-to-image diffu- sion models via initial noise optimization.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Initno: Boosting text-to-image diffu- sion models via initial noise optimization

Reference 17

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This paper cites Masked autoencoders are scalable vision learners.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Masked autoencoders are scalable vision learners

Reference 18

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This paper cites Learning deep representations by mutual information estimation and maximization.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Learning deep representations by mutual information estimation and maximization

Reference 19

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This paper cites Denoising diffu- sion probabilistic models.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Denoising diffu- sion probabilistic models

Reference 20

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This paper cites Imagen Video: High Definition Video Generation with Diffusion Models.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Imagen Video: High Definition Video Generation with Diffusion Models

Reference 21

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This paper cites Intriguing properties of generative classifiers.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Intriguing properties of generative classifiers

Reference 22

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This paper cites Learning discriminative latent attributes for zero- shot classification.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Learning discriminative latent attributes for zero- shot classification

Reference 23

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This paper cites Diffwave: A versatile diffusion model for audio synthesis.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Diffwave: A versatile diffusion model for audio synthesis

Reference 24

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This paper cites Learning multiple layers of features from tiny images.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Learning multiple layers of features from tiny images

Reference 25

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This paper cites Neural network classification and prior class probabilities.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Neural network classification and prior class probabilities

Reference 26

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This paper cites Robust inference via generative classifiers for handling noisy labels.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Robust inference via generative classifiers for handling noisy labels

Reference 27

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This paper cites Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, and Deepak Pathak.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, and Deepak Pathak

Reference 28

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This paper cites Are genera- tive classifiers more robust to adversarial attacks? In Interna- tional Conference on Machine Learning, pages 3804–3814.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Are genera- tive classifiers more robust to adversarial attacks? In Interna- tional Conference on Machine Learning, pages 3804–3814

Reference 29

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A Simple and Efficient Baseline for Zero-Shot Generative Classification Magic3d: High-resolution text- to-3d content creation

Reference 30

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This paper cites Alignment of diffusion models: Fundamentals, challenges, and future.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Alignment of diffusion models: Fundamentals, challenges, and future

Reference 31

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This paper cites Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps

Reference 32

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A Simple and Efficient Baseline for Zero-Shot Generative Classification DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models

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A Simple and Efficient Baseline for Zero-Shot Generative Classification Generative classifiers as a basis for trustwor- thy image classification

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This paper cites Costa: Co-occurrence statistics for zero-shot classification.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Costa: Co-occurrence statistics for zero-shot classification

Reference 35

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A Simple and Efficient Baseline for Zero-Shot Generative Classification On discriminative vs

Reference 36

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A Simple and Efficient Baseline for Zero-Shot Generative Classification GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

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A Simple and Efficient Baseline for Zero-Shot Generative Classification Glide: Towards photorealistic image gen- eration and editing with text-guided diffusion models

Reference 38

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A Simple and Efficient Baseline for Zero-Shot Generative Classification Automated flower classification over a large number of classes

Reference 39

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A Simple and Efficient Baseline for Zero-Shot Generative Classification Per- vasive label errors in test sets destabilize machine learning benchmarks

Reference 40

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A Simple and Efficient Baseline for Zero-Shot Generative Classification DINOv2: Learning Robust Visual Features without Supervision

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A Simple and Efficient Baseline for Zero-Shot Generative Classification WATT: Weight Average Test-Time Adaptation of CLIP

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A Simple and Efficient Baseline for Zero-Shot Generative Classification Cats and dogs

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A Simple and Efficient Baseline for Zero-Shot Generative Classification Scalable diffusion models with transformers

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This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

A Simple and Efficient Baseline for Zero-Shot Generative Classification SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 45

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A Simple and Efficient Baseline for Zero-Shot Generative Classification Dreamfusion: Text-to-3d using 2d diffusion

Reference 46

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This paper cites Aligning Text-to-Image Diffusion Models with Reward Backpropagation.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Aligning Text-to-Image Diffusion Models with Reward Backpropagation

Reference 47

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This paper cites Layered Rendering Diffusion Model for Controllable Zero-Shot Image Synthesis.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Layered Rendering Diffusion Model for Controllable Zero-Shot Image Synthesis

Reference 48

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This paper cites DiffTalker: Co-driven audio-image diffusion for talking faces via intermediate landmarks.

A Simple and Efficient Baseline for Zero-Shot Generative Classification DiffTalker: Co-driven audio-image diffusion for talking faces via intermediate landmarks

Reference 49

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This paper cites Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Not All Noises Are Created Equally:Diffusion Noise Selection and Optimization

Reference 50

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A Simple and Efficient Baseline for Zero-Shot Generative Classification Language models are unsuper- vised multitask learners

Reference 51

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This paper cites Learning transferable visual models from natural language supervi- sion.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Learning transferable visual models from natural language supervi- sion

Reference 52

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

A Simple and Efficient Baseline for Zero-Shot Generative Classification Zero-shot text-to-image generation

Reference 53

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This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 54

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

A Simple and Efficient Baseline for Zero-Shot Generative Classification High-resolution image synthesis with latent diffusion models

Reference 55

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This paper cites Pho- torealistic text-to-image diffusion models with deep language understanding.

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Reference 56

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Observation 21c5948a-9ea2-4b2e-8aeb-018d50995422 · outbound

This paper cites Adversarial Diffusion Distillation.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Adversarial Diffusion Distillation

Reference 57

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This paper cites Uncertainty-aware deep classifiers using generative models.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Uncertainty-aware deep classifiers using generative models

Reference 58

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Observation a44b70fd-2a31-4c19-bfb4-a39018ac4b57 · outbound

This paper cites IV-Mixed Sampler: Leveraging Image Diffusion Models for Enhanced Video Synthesis.

A Simple and Efficient Baseline for Zero-Shot Generative Classification IV-Mixed Sampler: Leveraging Image Diffusion Models for Enhanced Video Synthesis

Reference 59

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Reference 60

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This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Deep unsupervised learning using nonequilibrium thermodynamics

Reference 61

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This paper cites Generative modeling by estimating gradients of the data distribution.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Generative modeling by estimating gradients of the data distribution

Reference 62

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This paper cites Class- incremental learning with generative classifiers.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Class- incremental learning with generative classifiers

Reference 63

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Observation ef78446a-6ceb-43d2-a7d6-8ff4658e9709 · outbound

This paper cites Diffusion model align- ment using direct preference optimization.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Diffusion model align- ment using direct preference optimization

Reference 64

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Observation 53ac6bfc-7f24-4eec-b2e6-1a722217c218 · outbound

This paper cites A survey of zero-shot learning: Settings, methods, and ap- plications.

A Simple and Efficient Baseline for Zero-Shot Generative Classification A survey of zero-shot learning: Settings, methods, and ap- plications

Reference 65

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Observation 0776bae2-ca5e-4201-a5e3-3314f7df9462 · outbound

This paper cites Finetuned Language Models Are Zero-Shot Learners.

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Reference 66

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Reference 67

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Observation 07b2cbf5-37d3-4bb3-a0c3-23155bee8132 · outbound

This paper cites Latent embed- dings for zero-shot classification.

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Reference 68

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Observation fa190528-7e57-4ff7-b3d9-9f536cea9a12 · outbound

This paper cites Dream3d: Zero- shot text-to-3d synthesis using 3d shape prior and text-to- image diffusion models.

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Reference 69

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Observation 78c569e4-d1bd-4a81-9fdd-ae4b48f99446 · outbound

This paper cites Diffsound: Discrete diffusion model for text-to-sound generation.

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Reference 70

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Observation 61c2f545-6d1f-49e2-9ebd-2fa1dc968cfd · outbound

This paper cites Diffusion models: A comprehensive survey of methods and applications.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Diffusion models: A comprehensive survey of methods and applications

Reference 71

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Observation edee1427-655a-4bca-82a0-e455a28c99e8 · outbound

This paper cites Zero-shot classification with discriminative semantic representation learning.

A Simple and Efficient Baseline for Zero-Shot Generative Classification Zero-shot classification with discriminative semantic representation learning

Reference 72

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Observation f223504e-d4ba-4671-96f8-0f63c2bc3095 · outbound

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Reference 73

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Observation ce1e653f-8bba-4275-ac1a-7b1195875d73 · outbound

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Reference 74

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This paper cites Score-Based Generative Classifiers.

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Reference 75

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Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:27:18.506649Z

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