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Paper Citation Record · LEDGER

Addressing degeneracies in latent interpolation for diffusion models

As of 23 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2505.07481.

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pith.paper-citation-record.v1
2505.07481 v1

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measured 31 of 31 reference resolution

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Reference resolution

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Outbound references

Observation d117536f-0084-4d4b-aae7-45a859ea9fae · outbound

This paper cites Synthetic Data from Diffusion Models Improves ImageNet Classification.

Addressing degeneracies in latent interpolation for diffusion models Synthetic Data from Diffusion Models Improves ImageNet Classification

Reference 1

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This paper cites eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers.

Addressing degeneracies in latent interpolation for diffusion models eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers

Reference 2

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This paper cites ACM Transactions on Graphics (TOG)20(2), 95–126 (2001).

Addressing degeneracies in latent interpolation for diffusion models ACM Transactions on Graphics (TOG)20(2), 95–126 (2001)

Reference 3

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This paper cites In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

Addressing degeneracies in latent interpolation for diffusion models In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 4

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This paper cites In: Forty-first International Conference on Machine Learning (2024).

Addressing degeneracies in latent interpolation for diffusion models In: Forty-first International Conference on Machine Learning (2024)

Reference 5

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This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Addressing degeneracies in latent interpolation for diffusion models An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 6

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This paper cites ReNoise: Real Image Inversion Through Iterative Noising.

Addressing degeneracies in latent interpolation for diffusion models ReNoise: Real Image Inversion Through Iterative Noising

Reference 7

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This paper cites Advances in neural information processing systems30 (2017).

Addressing degeneracies in latent interpolation for diffusion models Advances in neural information processing systems30 (2017)

Reference 8

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This paper cites Advances in neural information processing systems33, 6840–6851 (2020).

Addressing degeneracies in latent interpolation for diffusion models Advances in neural information processing systems33, 6840–6851 (2020)

Reference 9

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition.

Addressing degeneracies in latent interpolation for diffusion models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition

Reference 10

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This paper cites In: The Twelfth International Conference on Learning Representations (2023).

Addressing degeneracies in latent interpolation for diffusion models In: The Twelfth International Conference on Learning Representations (2023)

Reference 11

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This paper cites Tiny models from tiny data: Textual and null-text inversion for few-shot distillation.

Addressing degeneracies in latent interpolation for diffusion models Tiny models from tiny data: Textual and null-text inversion for few-shot distillation

Reference 12

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This paper cites Common Diffusion Noise Schedules and Sample Steps are Flawed.

Addressing degeneracies in latent interpolation for diffusion models Common Diffusion Noise Schedules and Sample Steps are Flawed

Reference 13

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Addressing degeneracies in latent interpolation for diffusion models Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

Reference 14

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Addressing degeneracies in latent interpolation for diffusion models Lightning-Fast Image Inversion and Editing for Text-to-Image Diffusion Models

Reference 15

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Addressing degeneracies in latent interpolation for diffusion models Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Models

Reference 16

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This paper cites In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Addressing degeneracies in latent interpolation for diffusion models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 17

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Addressing degeneracies in latent interpolation for diffusion models In: Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 18

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Addressing degeneracies in latent interpolation for diffusion models In: International conference on machine learning

Reference 19

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Addressing degeneracies in latent interpolation for diffusion models Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 20

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Addressing degeneracies in latent interpolation for diffusion models In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 21

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Addressing degeneracies in latent interpolation for diffusion models In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, Oc- tober 5-9, 2015, Proceedings, Part III 18

Reference 22

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Addressing degeneracies in latent interpolation for diffusion models Advances in Neural Information Processing Systems35, 36479–36494 (2022)

Reference 23

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Addressing degeneracies in latent interpolation for diffusion models Advances in Neural Information Processing Systems 36 (2024)

Reference 24

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Addressing degeneracies in latent interpolation for diffusion models In: Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Addressing degeneracies in latent interpolation for diffusion models In: Proceedings of the 12th annual conference on Computer graphics and interactive techniques

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Addressing degeneracies in latent interpolation for diffusion models Denoising Diffusion Implicit Models

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Addressing degeneracies in latent interpolation for diffusion models In: Proceedings of the International Conference of Learning Representations (2024)

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Addressing degeneracies in latent interpolation for diffusion models In: International Conference on Machine Learning

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Addressing degeneracies in latent interpolation for diffusion models IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion Models

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Addressing degeneracies in latent interpolation for diffusion models goldfish

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