Paper Citation Record · LEDGER

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity

As of 20 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2506.01493.

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

pith.paper-citation-record.v1
2506.01493 v1

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Reference resolution

50 of 50 outbound references displayed

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Outbound references

Observation 1b22c9ac-6ae1-403d-9e50-75fe9c96eb70 · outbound

This paper cites Denoising diffusion probabilistic models,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Denoising diffusion probabilistic models,

Reference 1

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Observation 9997d9fd-335a-4f22-af5b-d1f1dc568e0c · outbound

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

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity High- resolution image synthesis with latent diffusion models,

Reference 2

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Observation 6d53184c-9fee-43e8-bdba-11a31af62178 · outbound

This paper cites Hierarchical text-conditional image generation with clip latents,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Hierarchical text-conditional image generation with clip latents,

Reference 3

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This paper cites Generative adversarial nets,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Generative adversarial nets,

Reference 4

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Observation e25ee1ed-c177-45e2-8a00-57d2cea3dd07 · outbound

This paper cites Tackling the generative learning trilemma with denoising diffusion GANs,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Tackling the generative learning trilemma with denoising diffusion GANs,

Reference 5

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Observation f3185b3d-bd33-4156-88ef-818024050e7b · outbound

This paper cites StyleGAN- t: Unlocking the power of GANs for fast large-scale text-to-image synthesis,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity StyleGAN- t: Unlocking the power of GANs for fast large-scale text-to-image synthesis,

Reference 6

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Observation c5eeeb49-5364-4eb2-a336-c19be6c5328e · outbound

This paper cites Scaling up gans for text-to-image synthesis,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Scaling up gans for text-to-image synthesis,

Reference 7

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This paper cites BK-SDM: A Lightweight, Fast, and Cheap Version of Stable Diffusion.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity BK-SDM: A Lightweight, Fast, and Cheap Version of Stable Diffusion

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Observation bb6a810a-17d4-4a66-8871-fa61062f202e · outbound

This paper cites Emerging properties in self-supervised vision transformers,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Emerging properties in self-supervised vision transformers,

Reference 9

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Observation 7265201f-95b0-4ccd-b516-1c21c0d4ea3e · outbound

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

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Learning transferable visual models from natural language supervi- sion,

Reference 10

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Observation 178ea243-6291-49eb-a389-476dd8d1071a · outbound

This paper cites Projected gans con- verge faster,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Projected gans con- verge faster,

Reference 11

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Observation b5c9e820-77bd-455a-88d3-4b910e75f212 · outbound

This paper cites Galip: Generative adversarial clips for text-to-image synthesis,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Galip: Generative adversarial clips for text-to-image synthesis,

Reference 12

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This paper cites SAN: Inducing metrizability of GAN with discrimi- native normalized linear layer,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity SAN: Inducing metrizability of GAN with discrimi- native normalized linear layer,

Reference 13

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This paper cites A mathematical theory of communication,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity A mathematical theory of communication,

Reference 14

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This paper cites A question of terminology,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity A question of terminology,

Reference 15

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Observation 81dcc0b4-a22d-4ce2-b612-0dd0a1749406 · outbound

This paper cites Mutual information neural estimation,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Mutual information neural estimation,

Reference 16

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Observation 25577f8d-ed32-4ca5-ae12-afd3f77cf041 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Gans trained by a two time-scale update rule converge to a local nash equilibrium,

Reference 17

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This paper cites Improved techniques for training gans,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Improved techniques for training gans,

Reference 18

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This paper cites Df- gan: A simple and effective baseline for text-to-image synthesis,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Df- gan: A simple and effective baseline for text-to-image synthesis,

Reference 19

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity A style-based generator architecture for generative adversarial networks,

Reference 20

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This paper cites Understanding noise injection in gans,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Understanding noise injection in gans,

Reference 21

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This paper cites The caltech-ucsd birds-200-2011 dataset,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity The caltech-ucsd birds-200-2011 dataset,

Reference 22

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Improving the improved training of wasserstein GANs,

Reference 23

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Unpaired image-to-image translation using cycle-consistent adversarial networks,

Reference 24

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Taming transformers for high- resolution image synthesis,

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Auto-encoding variational bayes,

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Generating sentences from a continuous space,

Reference 27

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Infogan: interpretable representation learning by information maximizing generative adversarial nets,

Reference 28

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity The im algorithm: a variational approach to information maximization,

Reference 29

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity The unreasonable effectiveness of deep features as a perceptual metric,

Reference 30

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This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject- driven generation,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Dreambooth: Fine tuning text-to-image diffusion models for subject- driven generation,

Reference 31

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity DINOv2: Learning robust visual features without supervision,

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Vision transformers need registers,

Reference 33

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This paper cites The role of imagenet classes in fr ´echet inception distance,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity The role of imagenet classes in fr ´echet inception distance,

Reference 34

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Openclip,

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Microsoft coco: Common objects in context,

Reference 36

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts,

Reference 37

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This paper cites Classifier-free diffusion guidance,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Classifier-free diffusion guidance,

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Generative adversarial text to image synthesis,

Reference 39

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This paper cites Stackgan: Text to photo-realistic image synthesis with stacked gener- ative adversarial networks,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Stackgan: Text to photo-realistic image synthesis with stacked gener- ative adversarial networks,

Reference 40

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Attngan: Fine-grained text to image generation with attentional generative adversarial networks,

Reference 41

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This paper cites Cross-modal contrastive learning for text-to-image generation,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Cross-modal contrastive learning for text-to-image generation,

Reference 42

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This paper cites Towards language-free training for text-to-image generation,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Towards language-free training for text-to-image generation,

Reference 43

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This paper cites Spectral normal- ization for generative adversarial networks,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Spectral normal- ization for generative adversarial networks,

Reference 44

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This paper cites Progressive growing of GANs for improved quality, stability, and variation,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Progressive growing of GANs for improved quality, stability, and variation,

Reference 45

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Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity De- mystifying MMD GANs,

Reference 46

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This paper cites Assessing Generative Models via Precision and Recall,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Assessing Generative Models via Precision and Recall,

Reference 47

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This paper cites Kynk ¨a¨anniemi, T.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Kynk ¨a¨anniemi, T

Reference 48

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This paper cites Feature likelihood score: Evaluating the generalization of generative models using samples,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Feature likelihood score: Evaluating the generalization of generative models using samples,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:25.172225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3ac4314f-88f1-4ba7-8668-d9d4a18f2ca9 · outbound

This paper cites Holistic evaluation of text-to-image models,.

Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity Holistic evaluation of text-to-image models,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:48:25.087291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 56d8ec0f-4eb1-4c16-b5f3-bfcd2d32aa12 · inbound

Unifying Contrastive and Generative Objectives for Visual Understanding and Text-to-Image Generation cites this paper.

Unifying Contrastive and Generative Objectives for Visual Understanding and Text-to-Image Generation Efficiency without Compromise: CLIP-aided Text-to-Image GANs with Increased Diversity

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-21T12:20:07.003891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.