HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories | Paper Citation Record

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This paper cites Learning representations and generative models for 3d point clouds.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Learning representations and generative models for 3d point clouds

Reference 1

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This paper cites Hyperfields: To- wards zero-shot generation of nerfs from text.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Hyperfields: To- wards zero-shot generation of nerfs from text

Reference 2

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This paper cites Dickson, Ryan M.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Dickson, Ryan M

Reference 3

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HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Unresolved cited work

Reference 4

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This paper cites Emerg- ing properties in self-supervised vision transformers.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Emerg- ing properties in self-supervised vision transformers

Reference 5

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This paper cites Stargan v2: Diverse image synthesis for multiple domains.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Stargan v2: Diverse image synthesis for multiple domains

Reference 6

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This paper cites Objaverse: A universe of annotated 3d objects.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Objaverse: A universe of annotated 3d objects

Reference 7

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This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 8

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This paper cites Interpreting the Weight Space of Customized Diffusion Models.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Interpreting the Weight Space of Customized Diffusion Models

Reference 9

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This paper cites Implicit generation and mod- eling with energy based models.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Implicit generation and mod- eling with energy based models

Reference 10

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This paper cites Hyperdiffusion: Generating implicit neural fields with weight-space diffusion.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Hyperdiffusion: Generating implicit neural fields with weight-space diffusion

Reference 11

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This paper cites One Step Diffusion via Shortcut Models.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories One Step Diffusion via Shortcut Models

Reference 12

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This paper cites An image is worth one word: Personalizing text-to-image gen- eration using textual inversion.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories An image is worth one word: Personalizing text-to-image gen- eration using textual inversion

Reference 13

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This paper cites Learning energy-based models by dif- fusion recovery likelihood.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Learning energy-based models by dif- fusion recovery likelihood

Reference 14

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This paper cites Atlasnet: A papier-m ˆach´e ap- proach to learning 3d surface generation.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Atlasnet: A papier-m ˆach´e ap- proach to learning 3d surface generation

Reference 15

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This paper cites Hypernetworks.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Hypernetworks

Reference 16

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This paper cites Denoising dif- fusion probabilistic models.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Denoising dif- fusion probabilistic models

Reference 17

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This paper cites LoRA: Low-rank adaptation of large language models.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories LoRA: Low-rank adaptation of large language models

Reference 18

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This paper cites Mani- foldplus: A robust and scalable watertight manifold surface generation method for triangle soups.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Mani- foldplus: A robust and scalable watertight manifold surface generation method for triangle soups

Reference 19

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This paper cites Shap-e: Generating condi- tional 3d implicit functions.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Shap-e: Generating condi- tional 3d implicit functions

Reference 20

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

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Progressive growing of gans for improved quality, stability, and variation

Reference 21

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

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Elucidating the design space of diffusion-based generative models

Reference 22

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This paper cites Consistency trajectory mod- els: Learning probability flow ode trajectory of diffusion.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Consistency trajectory mod- els: Learning probability flow ode trajectory of diffusion

Reference 23

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This paper cites A tutorial on energy-based learn- ing.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories A tutorial on energy-based learn- ing

Reference 24

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HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Unresolved cited work

Reference 25

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This paper cites Zero-1-to- 3: Zero-shot one image to 3d object.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Zero-1-to- 3: Zero-shot one image to 3d object

Reference 26

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This paper cites Deep learning face attributes in the wild.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Deep learning face attributes in the wild

Reference 27

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This paper cites Marching cubes: A high resolution 3d surface construction algorithm.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Marching cubes: A high resolution 3d surface construction algorithm

Reference 28

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This paper cites Occupancy networks: Learning 3d reconstruction in function space.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Occupancy networks: Learning 3d reconstruction in function space

Reference 29

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This paper cites Deepsdf: Learning con- tinuous signed distance functions for shape representation.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Deepsdf: Learning con- tinuous signed distance functions for shape representation

Reference 30

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This paper cites Scalable diffusion models with transformers.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Scalable diffusion models with transformers

Reference 31

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This paper cites Hypermaml: Few-shot adaptation of deep models with hypernetworks.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Hypermaml: Few-shot adaptation of deep models with hypernetworks

Reference 32

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This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 33

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This paper cites Richdreamer: A generalizable normal-depth diffusion model for detail richness in text-to- 3d.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Richdreamer: A generalizable normal-depth diffusion model for detail richness in text-to- 3d

Reference 34

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This paper cites Learning transferable visual models from natural language supervision.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Learning transferable visual models from natural language supervision

Reference 35

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This paper cites Physics informed deep learning (part i): Data-driven solu- tions of nonlinear partial differential equations.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Physics informed deep learning (part i): Data-driven solu- tions of nonlinear partial differential equations

Reference 36

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This paper cites Physics informed deep learning (part ii): Data-driven discov- ery of nonlinear partial differential equations.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Physics informed deep learning (part ii): Data-driven discov- ery of nonlinear partial differential equations

Reference 37

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This paper cites Physics-informed neural networks: A deep learning frame- work for solving forward and inverse problems involving nonlinear partial differential equations.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Physics-informed neural networks: A deep learning frame- work for solving forward and inverse problems involving nonlinear partial differential equations

Reference 38

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

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories High-resolution image syn- thesis with latent diffusion models

Reference 39

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

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 40

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This paper cites Hyperdreambooth: Hypernetworks for fast personalization of text-to-image models.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Hyperdreambooth: Hypernetworks for fast personalization of text-to-image models

Reference 41

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This paper cites Learning representations by back-propagating er- rors.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Learning representations by back-propagating er- rors

Reference 42

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

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Photorealistic text-to-image diffusion models with deep language understanding

Reference 43

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This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Deep unsupervised learning using nonequilibrium thermodynamics

Reference 44

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This paper cites Generative modeling by esti- mating gradients of the data distribution.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Generative modeling by esti- mating gradients of the data distribution

Reference 45

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This paper cites Maximum likelihood training of score-based diffusion mod- els.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Maximum likelihood training of score-based diffusion mod- els

Reference 46

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This paper cites Consistency models.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Consistency models

Reference 47

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This paper cites A connection between score matching and denoising autoencoders.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories A connection between score matching and denoising autoencoders

Reference 48

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This paper cites Grewe, and Joao Sacramento.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Grewe, and Joao Sacramento

Reference 49

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This paper cites Pointflow: 3d point cloud generation with continuous normalizing flows.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Pointflow: 3d point cloud generation with continuous normalizing flows

Reference 50

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This paper cites Graph hy- pernetworks for neural architecture search.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Graph hy- pernetworks for neural architecture search

Reference 51

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Observation 3bd260b5-ba29-4811-9dbc-6c5cbb39aeef · outbound

This paper cites Reconstruction loss.

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Reconstruction loss

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.482333Z

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Observation 50a28051-1b22-40ab-8a83-2dbbb776a73e · outbound

This paper cites Specifically, we include images from the AFHQ dataset [6] sampled directly from the hypernetwork (Fig- ure 10) and after fast fine-tuning (Figure 11).

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories Specifically, we include images from the AFHQ dataset [6] sampled directly from the hypernetwork (Fig- ure 10) and after fast fine-tuning (Figure 11)

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T05:57:13.472667Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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