Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models | Paper Citation Record

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This paper cites Do GANs actually learn the distribution? An empirical study.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Do GANs actually learn the distribution? An empirical study

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This paper cites Neural sign actors: A diffusion model for 3d sign language production from text, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Neural sign actors: A diffusion model for 3d sign language production from text, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp

Reference 2

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This paper cites Au- tosimulate: (quickly) learning synthetic data generation, in: Computer Vision – ECCV 2020, pp.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Au- tosimulate: (quickly) learning synthetic data generation, in: Computer Vision – ECCV 2020, pp

Reference 3

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This paper cites Improving image generation with better captions.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Improving image generation with better captions

Reference 4

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This paper cites Sampling strate- gies for GAN synthetic data, in: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Sampling strate- gies for GAN synthetic data, in: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp

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This paper cites GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

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Reference 8

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This paper cites Subunets: End-to-end hand shape and continuous sign language recognition, in: 2017 IEEE International Conference on Computer Vision (ICCV), pp.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Subunets: End-to-end hand shape and continuous sign language recognition, in: 2017 IEEE International Conference on Computer Vision (ICCV), pp

Reference 9

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This paper cites A Survey on Generative Diffusion Model.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models A Survey on Generative Diffusion Model

Reference 10

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This paper cites OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields

Reference 11

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This paper cites Recognizing handshapes using small datasets, in: XXV Congreso Argentino de Ciencias de la Computaci´ on (CACIC 2019, Universidad Nacional de Ro Cuarto).

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Recognizing handshapes using small datasets, in: XXV Congreso Argentino de Ciencias de la Computaci´ on (CACIC 2019, Universidad Nacional de Ro Cuarto)

Reference 12

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This paper cites Lsa-t: The first continuous argen- tinian sign language dataset forsign language translation, in: Advances in Artificial Intelligence – IBERAMIA 2022, pp.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Lsa-t: The first continuous argen- tinian sign language dataset forsign language translation, in: Advances in Artificial Intelligence – IBERAMIA 2022, pp

Reference 13

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This paper cites Diffusion models beat gans on image synthesis.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Diffusion models beat gans on image synthesis

Reference 14

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This paper cites Sign language fingerspelling recogni- tion using synthetic data, in: Irish Conference on Artificial Intelligence and Cognitive Science.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Sign language fingerspelling recogni- tion using synthetic data, in: Irish Conference on Artificial Intelligence and Cognitive Science

Reference 15

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This paper cites Redes GANs como t´ ecnica de data augmentation para el reconocimiento de lengua de senas.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Redes GANs como t´ ecnica de data augmentation para el reconocimiento de lengua de senas

Reference 17

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This paper cites Unifying semi-supervised and robust learning by mixup.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Unifying semi-supervised and robust learning by mixup

Reference 18

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This paper cites Visual synthetic data generation for sign language recognition.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Visual synthetic data generation for sign language recognition

Reference 20

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This paper cites Synthetic data generation technique in signer-independent sign language recognition.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Synthetic data generation technique in signer-independent sign language recognition

Reference 21

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This paper cites Rebooting ACGAN: aux- iliary classifier gans with stable training.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Rebooting ACGAN: aux- iliary classifier gans with stable training

Reference 22

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This paper cites Hagrid – hand gesture recognition image dataset, in: Proceed- ings of the IEEE/CVF Winter Conference on Applications of Computer Vision (W ACV), pp.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Hagrid – hand gesture recognition image dataset, in: Proceed- ings of the IEEE/CVF Winter Conference on Applications of Computer Vision (W ACV), pp

Reference 23

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This paper cites Improving American Sign Language recognition with synthetic data, in: Proceedings of Machine Translation Summit XVII: Research Track, pp.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Improving American Sign Language recognition with synthetic data, in: Proceedings of Machine Translation Summit XVII: Research Track, pp

Reference 24

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This paper cites Quantitative Survey of the State of the Art in Sign Language Recognition.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Quantitative Survey of the State of the Art in Sign Language Recognition

Reference 25

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Reference 26

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This paper cites Robust learning from untrusted sources, in: International conference on machine learning, pp.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Robust learning from untrusted sources, in: International conference on machine learning, pp

Reference 27

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This paper cites Training Deep Face Recognition Systems with Synthetic Data.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Training Deep Face Recognition Systems with Synthetic Data

Reference 28

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This paper cites Imagenet classifica- tion with deep convolutional neural networks, in: Advances in Neural Information Processing Systems.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Imagenet classifica- tion with deep convolutional neural networks, in: Advances in Neural Information Processing Systems

Reference 29

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This paper cites Word-level deep sign lan- guage recognition from video: A new large-scale dataset and methods comparison, in: The IEEE Winter Conference on Applications of Com- puter Vision, pp.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Word-level deep sign lan- guage recognition from video: A new large-scale dataset and methods comparison, in: The IEEE Winter Conference on Applications of Com- puter Vision, pp

Reference 30

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Reference 31

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This paper cites Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Generating synthetic data with variational autoencoder to address class imbalance of graph attention network prediction model for construction management

Reference 32

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This paper cites A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial net- works for medical image synthesis.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial net- works for medical image synthesis

Reference 33

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This paper cites Reliable fidelity and diversity metrics for generative models, in: Proceedings of the 37th International Conference on Machine Learning.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Reliable fidelity and diversity metrics for generative models, in: Proceedings of the 37th International Conference on Machine Learning

Reference 34

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This paper cites Theoretical insights into memorization in gans, in: Neural Information Processing Systems Workshop, p.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Theoretical insights into memorization in gans, in: Neural Information Processing Systems Workshop, p

Reference 35

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Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models 728– 734

Reference 36

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Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Conditional Image Synthesis With Auxiliary Classifier GANs

Reference 37

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This paper cites Semantic image synthesis with spatially-adaptive normalization, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Semantic image synthesis with spatially-adaptive normalization, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp

Reference 38

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Reference 39

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This paper cites Hand shape recognition using very deep convolutional neural networks, in: Proceedings of the 1st Interna- tional Conference on Control and Computer Vision, p.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Hand shape recognition using very deep convolutional neural networks, in: Proceedings of the 1st Interna- tional Conference on Control and Computer Vision, p

Reference 40

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This paper cites A survey on sign language machine translation.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models A survey on sign language machine translation

Reference 41

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This paper cites A broad review on class imbalance learning techniques.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models A broad review on class imbalance learning techniques

Reference 42

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This paper cites Improved techniques for training gans, in: Proceedings of the 30th International Conference on Neural Information Processing Systems, p.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Improved techniques for training gans, in: Proceedings of the 30th International Conference on Neural Information Processing Systems, p

Reference 44

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This paper cites Learning from simulated and unsupervised images through ad- versarial training, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Learning from simulated and unsupervised images through ad- versarial training, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp

Reference 46

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This paper cites A survey of deep active learning.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models A survey of deep active learning

Reference 47

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This paper cites Improving diffusion models as an alternative to gans.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Improving diffusion models as an alternative to gans

Reference 49

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Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models High-Resolution Image Synthesis with Latent Diffusion Models

Reference 50

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This paper cites Signavatars: A large- scale 3d sign language holistic motion dataset andbenchmark, in: Pro- ceedings of the European Conference on Computer Vision (ECCV), pp.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Signavatars: A large- scale 3d sign language holistic motion dataset andbenchmark, in: Pro- ceedings of the European Conference on Computer Vision (ECCV), pp

Reference 51

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This paper cites A survey on generative adversarial networks for imbalance problems in computer vision tasks.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models A survey on generative adversarial networks for imbalance problems in computer vision tasks

Reference 52

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Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models How Does Mixup Help With Robustness and Generalization?

Reference 53

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Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Efficientnetv2: Smaller models and faster train- ing, in: International conference on machine learning, pp

Reference 54

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Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Random erasing data augmentation

Reference 55

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Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Domain general- ization: A survey

Reference 56

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This paper cites Generative adver- sarial network with transformer generator for boosting ecg classification.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Generative adver- sarial network with transformer generator for boosting ecg classification

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:03:49.066304Z

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This paper cites Learning to estimate 3d hand pose from single RGB images, in: Proceedings of the IEEE international conference on computer vision, pp.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models Learning to estimate 3d hand pose from single RGB images, in: Proceedings of the IEEE international conference on computer vision, pp

Reference 58

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Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models mixup: Beyond Empirical Risk Minimization

Reference 59

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Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models An overview of multi-task learning

Reference 61

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Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models A gan-based hybrid sampling method for imbalanced customer classification

Reference 64

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Reference 2017

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Reference 2018

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Reference 2019

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This paper cites ACM Trans.

Bringing Balance to Hand Shape Classification: Mitigating Data Imbalance Through Generative Models ACM Trans

Reference 2021

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