Frequency Composition for Compressed and Domain-Adaptive Neural Networks | Paper Citation Record

Observation 9315facc-395a-466d-99fb-b8b1f60cc64d · outbound

This paper cites R2snet: Scalable domain adaptation for object detection in cloud– based robotic ecosystems via proposal refinement.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks R2snet: Scalable domain adaptation for object detection in cloud– based robotic ecosystems via proposal refinement

Reference 1

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This paper cites QGen: On the Ability to Generalize in Quantization Aware Training.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks QGen: On the Ability to Generalize in Quantization Aware Training

Reference 2

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Frequency Composition for Compressed and Domain-Adaptive Neural Networks Unresolved cited work

Reference 3

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This paper cites Parameter-free online test-time adaptation.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Parameter-free online test-time adaptation

Reference 4

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This paper cites Prentice-Hall, Inc., 1988.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Prentice-Hall, Inc., 1988

Reference 5

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This paper cites Pasta: Proportional amplitude spectrum training augmentation for syn-to- real domain generalization.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Pasta: Proportional amplitude spectrum training augmentation for syn-to- real domain generalization

Reference 6

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This paper cites Amplitude-phase recombina- tion: Rethinking robustness of convolutional neural networks in frequency domain.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Amplitude-phase recombina- tion: Rethinking robustness of convolutional neural networks in frequency domain

Reference 7

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This paper cites Binaryconnect: Training deep neural networks with binary weights during propagations.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Binaryconnect: Training deep neural networks with binary weights during propagations

Reference 8

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Observation b76fe18f-5ac7-4808-972b-933fe338b219 · outbound

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

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Imagenet: A large-scale hierarchi- cal image database

Reference 9

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Observation 71d6a732-72b7-470d-b92c-21e7eb3600e3 · outbound

This paper cites Learned Step Size Quantization.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Learned Step Size Quantization

Reference 10

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Observation f9ab9b7d-ad7b-49e0-959d-1e8318e7f1d3 · outbound

This paper cites Sharpness-Aware Minimization for Efficiently Improving Generalization.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Sharpness-Aware Minimization for Efficiently Improving Generalization

Reference 11

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This paper cites A survey of quantization methods for efficient neural network inference.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks A survey of quantization methods for efficient neural network inference

Reference 12

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This paper cites Note: Robust continual test-time adaptation against temporal corre- lation.Advances in Neural Information Processing Systems, 35:27253–27266, 2022.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Note: Robust continual test-time adaptation against temporal corre- lation.Advances in Neural Information Processing Systems, 35:27253–27266, 2022

Reference 13

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This paper cites Sotta: Robust test-time adaptation on noisy data streams.Advances in Neural Information Processing Systems, 36, 2024.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Sotta: Robust test-time adaptation on noisy data streams.Advances in Neural Information Processing Systems, 36, 2024

Reference 14

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This paper cites Deep residual learning for image recognition.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Deep residual learning for image recognition

Reference 15

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Observation bc74a24f-43bf-4d4d-9f7a-66fa4b0ac587 · outbound

This paper cites Identity mappings in deep residual networks.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Identity mappings in deep residual networks

Reference 16

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Observation 41ff5cf6-4bcd-460d-9a12-0bbcdb7c2cf9 · outbound

This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 17

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This paper cites The many faces of robustness: A critical analysis of out- of-distribution generalization.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks The many faces of robustness: A critical analysis of out- of-distribution generalization

Reference 18

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This paper cites Searching for mobilenetv3.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Searching for mobilenetv3

Reference 19

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Observation f6734c4e-2338-473f-a5cb-fe66f32d5075 · outbound

This paper cites Daformer: Improving network architectures and train- ing strategies for domain-adaptive semantic segmen- tation.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Daformer: Improving network architectures and train- ing strategies for domain-adaptive semantic segmen- tation

Reference 20

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Observation e4cd9cb0-9bc8-4622-a663-1885ac791475 · outbound

This paper cites Fsdr: Frequency space domain randomiza- tion for domain generalization.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Fsdr: Frequency space domain randomiza- tion for domain generalization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:42.648832Z

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This paper cites Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm Surveillance.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm Surveillance

Reference 22

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This paper cites QT-DoG: Quantization-aware Training for Domain Generalization.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks QT-DoG: Quantization-aware Training for Domain Generalization

Reference 23

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Observation baa27e73-ae05-4388-b688-b7c843574fe5 · outbound

This paper cites Neural network quantization with scale- adjusted training.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Neural network quantization with scale- adjusted training

Reference 24

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

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Learning multiple layers of features from tiny images

Reference 25

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This paper cites Visualizing the loss landscape of neural nets.Advances in neural information process- ing systems, 31, 2018.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Visualizing the loss landscape of neural nets.Advances in neural information process- ing systems, 31, 2018

Reference 26

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Observation 42a69a13-0f71-4f89-95a8-721125c8536a · outbound

This paper cites The norm must go on: Dynamic unsupervised domain adaptation by normalization.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks The norm must go on: Dynamic unsupervised domain adaptation by normalization

Reference 27

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Observation 8c281d2e-f855-43ee-9965-8783ff20156b · outbound

This paper cites Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

Reference 28

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Observation 21774c06-8e05-43ff-9753-eaf4c5423a12 · outbound

This paper cites Efficient test-time model adaptation without forget- ting.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Efficient test-time model adaptation without forget- ting

Reference 29

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Observation b99b8908-80b9-4145-a398-fcbc48d532e2 · outbound

This paper cites Towards Stable Test-Time Adaptation in Dynamic Wild World.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Towards Stable Test-Time Adaptation in Dynamic Wild World

Reference 30

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This paper cites Improving robustness against common corruptions by covariate shift adaptation.Advances in neural infor- mation processing systems, 33:11539–11551, 2020.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Improving robustness against common corruptions by covariate shift adaptation.Advances in neural infor- mation processing systems, 33:11539–11551, 2020

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:41.557346Z

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This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:41.387878Z

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Observation ac92e604-bfb1-45e3-85a7-cf05c8a39fbf · outbound

This paper cites Visu- alizing data using t-sne.Journal of machine learning research, 9(11), 2008.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Visu- alizing data using t-sne.Journal of machine learning research, 9(11), 2008

Reference 33

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Observation 233b2473-73d7-43c2-857d-c4d8f1affe49 · outbound

This paper cites Tent: Fully Test-time Adaptation by Entropy Minimization.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 34

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Observation e272d6ce-a9fd-40fc-beae-930648f7d425 · outbound

This paper cites Learning robust global representations by penalizing local predictive power.Advances in neural information processing systems, 32, 2019.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Learning robust global representations by penalizing local predictive power.Advances in neural information processing systems, 32, 2019

Reference 35

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Observation 57194cac-fc3a-49a1-a1cd-2fe386ceff02 · outbound

This paper cites High-frequency component helps explain the generalization of convolutional neural networks.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks High-frequency component helps explain the generalization of convolutional neural networks

Reference 36

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This paper cites Generalizing to unseen do- mains: A survey on domain generalization.IEEE transactions on knowledge and data engineering, 35 (8):8052–8072, 2022.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Generalizing to unseen do- mains: A survey on domain generalization.IEEE transactions on knowledge and data engineering, 35 (8):8052–8072, 2022

Reference 37

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Observation 6eae89a0-08d2-4fa9-9d87-6e1cbc1e6bbc · outbound

This paper cites Continual test-time domain adaptation.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Continual test-time domain adaptation

Reference 38

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Observation 954f1646-94f2-48dc-910f-041b698b5673 · outbound

This paper cites Efficienttrain: Exploring generalized curriculum learning for training visual backbones.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Efficienttrain: Exploring generalized curriculum learning for training visual backbones

Reference 39

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Observation 1f161f55-d58b-4962-9ec0-5b040dee582a · outbound

This paper cites Learning in the fre- quency domain.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Learning in the fre- quency domain

Reference 40

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Observation 38fd1400-4879-4964-8cbc-5d893c7c9cf2 · outbound

This paper cites A fourier-based framework for do- main generalization.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks A fourier-based framework for do- main generalization

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Observation 91d81ea0-c600-42b5-87ba-c0632e56b1af · outbound

This paper cites Fda: Fourier do- main adaptation for semantic segmentation.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Fda: Fourier do- main adaptation for semantic segmentation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:39.724400Z

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Observation 3c92e95a-3ffa-4b3f-8efb-a6754a9a2a7b · outbound

This paper cites A fourier per- 10 spective on model robustness in computer vision.Ad- vances in Neural Information Processing Systems, 32,.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks A fourier per- 10 spective on model robustness in computer vision.Ad- vances in Neural Information Processing Systems, 32,

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Observation 6c15b53c-08ad-473d-9106-f5ebe26fa752 · outbound

This paper cites Adapt-net: A unified ob- ject detection framework for mobile augmented real- ity.IEEE Access, 2024.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Adapt-net: A unified ob- ject detection framework for mobile augmented real- ity.IEEE Access, 2024

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:50:39.310784Z

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Observation 2397c9a8-6363-41c8-9c02-1292942af991 · outbound

This paper cites Lq-nets: Learned quantization for highly accurate and compact deep neural networks.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Lq-nets: Learned quantization for highly accurate and compact deep neural networks

Reference 45

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Observation ecb783f7-0580-49a8-964a-2311f51648a5 · outbound

This paper cites Why Quantization Improves Generalization: NTK of Binary Weight Neural Networks.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Why Quantization Improves Generalization: NTK of Binary Weight Neural Networks

Reference 46

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This paper cites Memo: Test time robustness via adaptation and aug- mentation.Advances in neural information processing systems, 35:38629–38642, 2022.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Memo: Test time robustness via adaptation and aug- mentation.Advances in neural information processing systems, 35:38629–38642, 2022

Reference 47

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Observation 01277e7c-aeae-4cfa-a935-b2b01de49df6 · outbound

This paper cites A review of single-source deep unsupervised visual domain adaptation.IEEE Trans- actions on Neural Networks and Learning Systems, 33 (2):473–493, 2020.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks A review of single-source deep unsupervised visual domain adaptation.IEEE Trans- actions on Neural Networks and Learning Systems, 33 (2):473–493, 2020

Reference 48

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Observation c308a2ff-d656-4949-b78d-e10094e49960 · outbound

This paper cites DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 49

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Observation aee9d3d7-99df-475a-a473-f0e7093e3365 · outbound

This paper cites Experimental Details We use pre-activation [16] based ResNet [15] models.

Frequency Composition for Compressed and Domain-Adaptive Neural Networks Experimental Details We use pre-activation [16] based ResNet [15] models

Reference 2016

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