Paper Citation Record · LEDGER

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding

As of 19 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 3 inbound Pith citation observations for arXiv:2504.17219.

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

pith.paper-citation-record.v1
2504.17219 v1

Coverage vector

measured 43 of 43 reference resolution

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measured 46 of 46 standing notices

One-hop event checks from named stored sources.

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:49:25.375791Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-22T14:34:54.950360Z

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation 5f04c61d-dafb-4180-9f75-5c497cfa26e0 · outbound

This paper cites Impress: Evaluating the resilience of imperceptible perturbations against unauthorized data usage in diffusion-based generative ai.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Impress: Evaluating the resilience of imperceptible perturbations against unauthorized data usage in diffusion-based generative ai

Reference 1

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Observation d4b96b5a-fcda-4df4-8915-1b38aba5ee35 · outbound

This paper cites Unlabeled data improves adversar- ial robustness.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Unlabeled data improves adversar- ial robustness

Reference 2

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

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Imagenet: A large-scale hierarchical image database

Reference 3

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

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Taming transformers for high-resolution image synthesis

Reference 4

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Observation 31a526be-36e2-4331-943b-3e55dc8d1f24 · outbound

This paper cites Explaining and harnessing adversarial examples.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Explaining and harnessing adversarial examples

Reference 5

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Observation 9bb02588-90f3-4c10-8ca9-0047dbff3479 · outbound

This paper cites Im- proving robustness using generated data.Advances in Neural Information Processing Systems, 2021.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Im- proving robustness using generated data.Advances in Neural Information Processing Systems, 2021

Reference 6

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Observation 8040a382-4659-4d3e-b493-14db876e57f0 · outbound

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

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Gans trained by a two time-scale update rule converge to a local nash equilib- rium

Reference 7

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Observation faeb6d4a-acfa-4783-b5e5-a6bcfa37100c · outbound

This paper cites Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI

Reference 8

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Observation be174fba-c774-4387-8c3f-83b0f933331a · outbound

This paper cites Robust pre-training by adversarial contrastive learn- ing.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Robust pre-training by adversarial contrastive learn- ing

Reference 9

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Observation 9076d8e1-6b54-4d0c-9f2c-6f4763ca7e2e · outbound

This paper cites Adversar- ial self-supervised contrastive learning.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Adversar- ial self-supervised contrastive learning

Reference 10

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Observation d9db0fe1-1909-4569-9e11-f71c0005d490 · outbound

This paper cites Effective targeted attacks for adversarial self- supervised learning.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Effective targeted attacks for adversarial self- supervised learning

Reference 11

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This paper cites An introduction to variational autoencoders.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding An introduction to variational autoencoders

Reference 12

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Observation 742b78d0-1f0e-4998-9da2-cd7fb19cf4d7 · outbound

This paper cites EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling

Reference 13

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Observation 53b1453f-2e23-4548-ade7-9784e337aa47 · outbound

This paper cites Visualizing the loss landscape of neural nets.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Visualizing the loss landscape of neural nets

Reference 14

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Observation 4340854f-5a2e-4ae5-9a63-edc15ccad9b0 · outbound

This paper cites Autoregressive image generation without vec- tor quantization.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Autoregressive image generation without vec- tor quantization

Reference 15

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Observation 478d2a51-f9e0-49d2-865d-620fabfb142f · outbound

This paper cites Mist: Towards Improved Adversarial Examples for Diffusion Models.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Mist: Towards Improved Adversarial Examples for Diffusion Models

Reference 16

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This paper cites Microsoft coco: Common objects in context.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Microsoft coco: Common objects in context

Reference 17

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Observation 8a82d371-ddc7-4651-9031-828d00b78df6 · outbound

This paper cites Disguised copyright infringement of latent diffusion models.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Disguised copyright infringement of latent diffusion models

Reference 18

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This paper cites Sit: Explor- ing flow and diffusion-based generative models with scalable interpolant transformers.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Sit: Explor- ing flow and diffusion-based generative models with scalable interpolant transformers

Reference 19

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This paper cites Towards deep learn- ing models resistant to adversarial attacks.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Towards deep learn- ing models resistant to adversarial attacks

Reference 20

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This paper cites Diffusion models for ad- versarial purification.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Diffusion models for ad- versarial purification

Reference 21

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This paper cites Scalable diffusion models with transformers.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Scalable diffusion models with transformers

Reference 22

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This paper cites Learn- ing transferable visual models from natural language super- vision.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Learn- ing transferable visual models from natural language super- vision

Reference 23

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

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding High-resolution image syn- thesis with latent diffusion models

Reference 24

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This paper cites Improved techniques for training gans.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Improved techniques for training gans

Reference 25

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This paper cites Raising the cost of malicious ai-powered image editing.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Raising the cost of malicious ai-powered image editing

Reference 26

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

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 27

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This paper cites Glaze: Protecting artists 9 from style mimicry by{Text-to-Image} models.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Glaze: Protecting artists 9 from style mimicry by{Text-to-Image} models

Reference 28

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This paper cites Nightshade: Prompt- specific poisoning attacks on text-to-image generative mod- els.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Nightshade: Prompt- specific poisoning attacks on text-to-image generative mod- els

Reference 29

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 30

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Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Improving the Diffusability of Autoencoders

Reference 31

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This paper cites Intriguing properties of neural networks.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Intriguing properties of neural networks

Reference 32

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Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Neural discrete representation learning

Reference 33

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This paper cites Improving adversarial robustness requires revisiting misclassified examples.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Improving adversarial robustness requires revisiting misclassified examples

Reference 34

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Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Sheikh, and Eero P

Reference 35

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This paper cites Adversarial weight perturbation helps robust generalization.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Adversarial weight perturbation helps robust generalization

Reference 36

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This paper cites Denoising diffusion autoencoders are unified self-supervised learners.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Denoising diffusion autoencoders are unified self-supervised learners

Reference 37

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This paper cites Representation alignment for generation: Training diffusion transformers is easier than you think.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Representation alignment for generation: Training diffusion transformers is easier than you think

Reference 38

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This paper cites Theoretically prin- cipled trade-off between robustness and accuracy.

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Theoretically prin- cipled trade-off between robustness and accuracy

Reference 39

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

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding The unreasonable effectiveness of deep features as a perceptual metric

Reference 40

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This paper cites A photo of [C].

Enhancing Variational Autoencoders with Smooth Robust Latent Encoding A photo of [C]

Reference 41

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Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Unresolved cited work

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Enhancing Variational Autoencoders with Smooth Robust Latent Encoding Cake” “Car

Reference 43

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Pith citing papers

Observation 4738d2b0-1ca0-4921-ae91-287aa715629d · inbound

LatentStealth: Unnoticeable and Efficient Adversarial Attacks on Expressive Human Pose and Shape Estimation cites this paper.

LatentStealth: Unnoticeable and Efficient Adversarial Attacks on Expressive Human Pose and Shape Estimation Enhancing Variational Autoencoders with Smooth Robust Latent Encoding

Reference 41

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LAFR: Efficient Diffusion-based Blind Face Restoration via Latent Codebook Alignment Adapter cites this paper.

LAFR: Efficient Diffusion-based Blind Face Restoration via Latent Codebook Alignment Adapter Enhancing Variational Autoencoders with Smooth Robust Latent Encoding

Reference 26

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Understanding Latent Diffusability via Fisher Geometry cites this paper.

Understanding Latent Diffusability via Fisher Geometry Enhancing Variational Autoencoders with Smooth Robust Latent Encoding

Reference 19

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