Differentiable Image Data Augmentation and Its Applications: A Survey
Pages 1148 - 1164
Abstract
Data augmentation is an effective method to improve model robustness and generalization. Conventional data augmentation pipelines are commonly used as preprocessing modules for neural networks with predefined heuristics and restricted differentiability. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the augmentation policy searching strategies. Some recent works indicated that the differentiable data augmentation (DDA) could effectively contribute to the training of neural networks and the searching of augmentation policy strategies. This survey provides a comprehensive and structured overview of the advances in DDA. Specifically, we focus on fundamental elements including differentiable operations, operation relaxations, and gradient estimations, then categorize existing DDA works accordingly, and investigate the utilization of DDA in selected of practical applications, specifically <italic>neural augmentation networks</italic> and <italic>differentiable augmentation search</italic>. Finally, we discuss current challenges of DDA and future research directions.
References
[1]
T. Dao, A. Gu, A. Ratner, V. Smith, C. De Sa, and C. Ré, “A kernel theory of modern data augmentation,” in Proc. Int. Conf. Mach. Learn., 2019, pp. 1528–1537.
[2]
L. Perez and J. Wang, “The effectiveness of data augmentation in image classification using deep learning,” 2017,.
[3]
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” 2017,.
[4]
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz, “mixup: Beyond empirical risk minimization,” 2017,.
[5]
S. Yun, D. Han, S. J. Oh, S. Chun, J. Choe, and Y. Yoo, “CutMix: Regularization strategy to train strong classifiers with localizable features,” in Proc. IEEE/CVF Int. Conf. Comput. Vis., 2019, pp. 6023–6032.
[6]
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets,” in Proc. Adv. Neural Inf. Process. Syst., 2016, pp. 2180–2188.
[7]
G. Ning, G. Chen, C. Tan, S. Luo, L. Bo, and H. Huang, “Data augmentation for object detection via differentiable neural rendering,” 2021,.
[8]
V. Verma et al., “Manifold mixup: Better representations by interpolating hidden states,” in Proc. Int. Conf. Mach. Learn., 2019, pp. 6438–6447.
[9]
T.-H. Cheung and D.-Y. Yeung, “MODALS: Modality-agnostic automated data augmentation in the latent space,” in Proc. Int. Conf. Learn. Representations, 2020.
[10]
A. Sinha, K. Ayush, J. Song, B. Uzkent, H. Jin, and S. Ermon, “Negative data augmentation,” 2021,.
[11]
D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C. A. Raffel, “MixMatch: A holistic approach to semi-supervised learning,” in Proc. Adv. Neural Inf. Process. Syst., 2019, pp. 5049–5059.
[12]
D. Berthelot et al., “ReMixMatch: Semi-supervised learning with distribution alignment and augmentation anchoring,” 2019,.
[13]
K. Sohn et al., “Fixmatch: Simplifying semi-supervised learning with consistency and confidence,” in Proc. Adv. Neural Inf. Process. Syst., 2020, pp. 596–608.
[14]
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in Proc. Int. Conf. Mach. Learn., 2020, pp. 1597–1607.
[15]
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in Proc. IEEE/CVF Conf. Comput. Vis. pattern Recognit., 2020, pp. 9729–9738.
[16]
J.-B. Grill et al., “Bootstrap your own latent-a new approach to self-supervised learning,” in Proc. Adv. Neural Inf. Process. Syst., 2020, pp. 21 271–21 284.
[17]
X. Chen and K. He, “Exploring simple siamese representation learning,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2021, pp. 15 750–15 758.
[18]
E. Riba, D. Mishkin, D. Ponsa, E. Rublee, and G. Bradski, “Kornia: An open source differentiable computer vision library for PyTorch,” in Proc. IEEE/CVF Winter Conf. Appl. Comput. Vis., 2020, pp. 3674–3683.
[19]
T.-M. Li, M. Gharbi, A. Adams, F. Durand, and J. Ragan-Kelley, “Differentiable programming for image processing and deep learning in Halide,” ACM Trans. Graph., vol. 37, no. 4, pp. 1–13, 2018.
[20]
K. M. Yi, Y. Verdie, P. Fua, and V. Lepetit, “Learning to assign orientations to feature points,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2016, pp. 107–116.
[21]
C. Bowles et al., “GAN augmentation: Augmenting training data using generative adversarial networks,” 2018,.
[22]
O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas, “DeblurGAN: Blind motion deblurring using conditional adversarial networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2018, pp. 8183–8192.
[23]
M. Gharbi, J. Chen, J. T. Barron, S. W. Hasinoff, and F. Durand, “Deep bilateral learning for real-time image enhancement,” ACM Trans. Graph., vol. 36, no. 4, pp. 1–12, 2017.
[24]
S. Iizuka, E. Simo-Serra, and H. Ishikawa, “Let there be color! Joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification,” ACM Trans. Graph., vol. 35, no. 4, pp. 1–11, 2016.
[25]
R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in Proc. Eur. Conf. Comput. Vis., 2016, pp. 649–666.
[26]
Y. Li et al., “DADA: Differentiable automatic data augmentation,” 2020,.
[27]
C. Rommel, T. Moreau, J. Paillard, and A. Gramfort, “CADDA: Class-wise automatic differentiable data augmentation for EEG signals,” in Proc. 9th Int. Conf. Learn. Representations, 2021.
[28]
R. Hataya, J. Zdenek, K. Yoshizoe, and H. Nakayama, “Faster autoaugment: Learning augmentation strategies using backpropagation,” in Proc. Eur. Conf. Comput. Vis., 2020, pp. 1–16.
[29]
R. Hataya, J. Zdenek, K. Yoshizoe, and H. Nakayama, “Meta approach to data augmentation optimization,” in Proc. IEEE/CVF Winter Conf. Appl. Comput. Vis., 2022, pp. 3535–3544.
[30]
E. Tseng et al., “Hyperparameter optimization in black-box image processing using differentiable proxies,” ACM Trans. Graph., vol. 38, no. 4, Jul. 2019, Art. no.
[31]
J. Li, K. A. Skinner, R. M. Eustice, and M. Johnson-Roberson, ”Watergan: Unsupervised generative network to enable real-time color correction of monocular underwater images,” IEEE Robot. Automat. Lett., vol. 3, no. 1, pp. 387–394, 2017.
[32]
A. Sablayrolles, M. Douze, C. Schmid, and H. Jégou, “Radioactive data: Tracing through training,” in Proc. Int. Conf. Mach. Learn., 2020, pp. 8326–8335.
[33]
C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” J. Big Data, vol. 6, no. 1, pp. 1–48, 2019.
[34]
M. Xu, S. Yoon, A. Fuentes, and D. S. Park, “A comprehensive survey of image augmentation techniques for deep learning,” 2022,.
[35]
Z. Yang, R. O. Sinnott, J. Bailey, and Q. Ke, “A survey of automated data augmentation algorithms for deep learning-based image classication tasks,” 2022,.
[36]
H. Naveed, “Survey: Image mixing and deleting for data augmentation,” 2021,.
[37]
J. Schulman, N. Heess, T. Weber, and P. Abbeel, “Gradient estimation using stochastic computation graphs,” in Proc. Adv. Neural Inf. Process. Syst., 2015, vol. 2, pp. 3528–3536.
[38]
W. Chen et al., “Learning to predict 3D objects with an interpolation-based differentiable renderer,” in Proc. Adv. Neural Inf. Process. Syst., 2019, pp. 9609–9619.
[39]
O. Michel, R. Bar-On, R. Liu, S. Benaim, and R. Hanocka, “Text2Mesh: Text-driven neural stylization for meshes,” 2021,.
[40]
N. Passalis et al., “OpenDR: An open toolkit for enabling high performance, low footprint deep learning for robotics,” 2022,.
[41]
M. Jaderberg et al., “Spatial transformer networks,” in Proc. Adv. Neural Inf. Process. Syst., 2015, vol. 2, pp. 2017–2025.
[42]
J. Shi, E. Riba, D. Mishkin, F. Moreno, and A. Nicolaou, “Differentiable data augmentation with Kornia,” 2020,.
[43]
P. Simard, D. Steinkraus, and J. Platt, “Best practices for convolutional neural networks applied to visual document analysis,” in Proc. 7th Int. Conf. Document Anal. Recognit., 2003, pp. 958–963.
[44]
J. Albers et al., “Elastic transformation of histological slices allows precise co-registration with microCT data sets for a refined virtual histology approach,” Sci. Rep., vol. 11, 2021, Art. no.
[45]
N. Aldoj, F. Biavati, F. Michallek, S. Stober, and M. Dewey, “Automatic prostate and prostate zones segmentation of magnetic resonance images using denseNET-like U-net,” Sci. Rep., vol. 10, 2020, Art. no.
[46]
R. Szeliski, Computer Vision: Algorithms and Applications. Berlin, Germany: Springer, 2010.
[47]
M. Avi-Aharon, A. Arbelle, and T. R. Raviv, “Differentiable histogram loss functions for intensity-based image-to-image translation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 10, pp. 11642–11653, 2023.
[48]
E. Ustinova and V. Lempitsky, “Learning deep embeddings with histogram loss,” in Proc. Adv. Neural Inf. Process. Syst., 2016, pp. 4177–4185.
[49]
M. Avi-Aharon, A. Arbelle, and T. R. Raviv, “Differentiable histogram loss functions for intensity-based image-to-image translation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 10, pp. 11642–11653, Oct. 2023.
[50]
D. Lewy and J. Mańdziuk, “An overview of mixing augmentation methods and augmentation strategies,” Artif. Intell. Rev., vol. 56, pp. 2111–2169, 2022.
[51]
H. Inoue, “Data augmentation by pairing samples for images classification,” 2018,.
[52]
A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “YOLOV4: Optimal speed and accuracy of object detection,” 2020,.
[53]
T. DeVries and G. W. Taylor, “Improved regularization of convolutional neural networks with cutout,” 2017,.
[54]
Z. Zhong, L. Zheng, G. Kang, S. Li, and Y. Yang, “Random erasing data augmentation,” in Proc. AAAI Conf. Artif. Intell., 2020, pp. 13 001–13 008.
[55]
R. G. Lopes, D. Yin, B. Poole, J. Gilmer, and E. D. Cubuk, “Improving robustness without sacrificing accuracy with patch Gaussian augmentation,” 2019,.
[56]
D. Li, J. Yang, K. Kreis, A. Torralba, and S. Fidler, “Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2021, pp. 8300–8311.
[57]
S. Gowal, S.-A. Rebuffi, O. Wiles, F. Stimberg, D. A. Calian, and T. A. Mann, “Improving robustness using generated data,” in Proc. Adv. Neural Inf. Process. Syst., 2021, pp. 4218–4233.
[58]
S. Azizi, S. Kornblith, C. Saharia, M. Norouzi, and D. J. Fleet, “Synthetic data from diffusion models improves imageNet classification,” 2023,.
[59]
D. Li, H. Ling, S. W. Kim, K. Kreis, S. Fidler, and A. Torralba, “BigDatasetGAN: Synthesizing imageNet with pixel-wise annotations,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2022, pp. 21 330–21 340.
[60]
Y. Tian, L. Fan, P. Isola, H. Chang, and D. Krishnan, “StableRep: Synthetic images from text-to-image models make strong visual representation learners,” 2023,.
[61]
M. Mirza and S. Osindero, “Conditional generative adversarial nets,” 2014,.
[62]
A. Antoniou, A. Storkey, and H. Edwards, “Data augmentation generative adversarial networks,” 2017,.
[63]
Y. Wang, A. Gonzalez-Garcia, D. Berga, L. Herranz, F. S. Khan, and J. V. D. Weijer, “MineGAN: Effective knowledge transfer from GANs to target domains with few images,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2020, pp. 9332–9341.
[64]
D. Bau et al., “Seeing what a gan cannot generate,” in Proc. IEEE/CVF Int. Conf. Comput. Vis., 2019, pp. 4502–4511.
[65]
J.-Y. Zhu, P. Krähenbühl, E. Shechtman, and A. A. Efros, “Generative visual manipulation on the natural image manifold,” in Proc. 14th Eur. Conf. Comput. Vis., Amsterdam, The Netherlands, 2016, pp. 597–613.
[66]
W. Xia, Y. Zhang, Y. Yang, J.-H. Xue, B. Zhou, and M.-H. Yang, “GAN inversion: A survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 3, pp. 3121–3138, Mar. 2022.
[67]
D. T. Nguyen et al., “Data augmentation for small face datasets and face verification by generative adversarial networks inversion,” in Proc. 13th Int. Conf. Knowl. Syst. Eng., 2021, pp. 1–6.
[68]
M. Golhar, T. L. Bobrow, S. Ngamruengphong, and N. J. Durr, “GAN inversion for data augmentation to improve colonoscopy lesion classification,” 2022,.
[69]
D. Epstein, T. Park, R. Zhang, E. Shechtman, and A. A. Efros, “BlobGAN: Spatially disentangled scene representations,” in Proc. Eur. Conf. Comput. Vis., 2022, pp. 616–635.
[70]
B. Kawar et al., “Imagic: Text-based real image editing with diffusion models,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2023, pp. 6007–6017.
[71]
L. Tsaban and A. Passos, “LEDITS: Real image editing with DDPM inversion and semantic guidance,” 2023,.
[72]
Y. Song, X. Shao, K. Chen, W. Zhang, Z. Jing, and M. Li, “CLIPVG: Text-guided image manipulation using differentiable vector graphics,” in Proc. AAAI Conf. Artif. Intell., 2023, pp. 2312–2320.
[73]
J. Zhu, Y. Shen, D. Zhao, and B. Zhou, “In-domain GAN inversion for real image editing,” in Proc. Eur. Conf. Comput. Vis., 2020, pp. 592–608.
[74]
C. Szegedy et al., “Intriguing properties of neural networks,” 2013,.
[75]
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” 2014,.
[76]
A. Shafahi et al., “Adversarial training for free!,” in Proc. Adv. Neural Inf. Process. Syst., 2019, pp. 3358–3369.
[77]
Y. Wang, D. Zou, J. Yi, J. Bailey, X. Ma, and Q. Gu, “Improving adversarial robustness requires revisiting misclassified examples,” Int. Conf. Learn. Representations, 2019.
[78]
C. Xie, Y. Wu, L. V. D. Maaten, A. L. Yuille, and K. He, “Feature denoising for improving adversarial robustness,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2019, pp. 501–509.
[79]
T. Miyato, S. -I. Maeda, M. Koyama, and S. Ishii, “Virtual adversarial training: A regularization method for supervised and semi-supervised learning,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 41, no. 8, pp. 1979–1993, 2018.
[80]
A. Chakraborty, M. Alam, V. Dey, A. Chattopadhyay, and D. Mukhopadhyay, “Adversarial attacks and defences: A survey,” 2018,.
[81]
Z. Qian, K. Huang, Q.-F. Wang, and X.-Y. Zhang, “A survey of robust adversarial training in pattern recognition: Fundamental, theory, and methodologies,” Pattern Recognit., vol. 131, 2022, Art. no. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0031320322003703
[82]
Y. Bengio, N. Léonard, and A. Courville, “Estimating or propagating gradients through stochastic neurons for conditional computation,” 2013,.
[83]
W. Grathwohl, D. Choi, Y. Wu, G. Roeder, and D. Duvenaud, “Backpropagation through the void: Optimizing control variates for black-box gradient estimation,” in Proc. 5th Int. Conf. Learn. Representations, 2017.
[84]
E. Jang, S. Gu, and B. Poole, “Categorical reparameterization with gumbel-softmax,” 2016,.
[85]
C. J. Maddison, D. Tarlow, and T. Minka, “A-star sampling,” in Proc. Adv. Neural Inf. Process. Syst., 2014, pp. 3086–3094.
[86]
J. Chung, S. Ahn, and Y. Bengio, “Hierarchical multiscale recurrent neural networks,” 2016,.
[87]
P. Yin, J. Lyu, S. Zhang, S. Osher, Y. Qi, and J. Xin, “Understanding straight-through estimator in training activation quantized neural nets,” 2019,.
[88]
J. Xu, M. Li, and Z. Zhu, “Automatic data augmentation for 3D medical image segmentation,” in Proc. Int. Conf. Med. Image Comput. Comput.- Assist. Intervention, 2020, pp. 378–387.
[89]
X. Zhang, Q. Wang, J. Zhang, and Z. Zhong, “Adversarial autoaugment,” 2019,.
[90]
C. Lin et al., “Online hyper-parameter learning for auto-augmentation strategy,” in Proc. IEEE/CVF Int. Conf. Comput. Vis., 2019, pp. 6578–6587.
[91]
R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Mach. Learn., vol. 8, no. 3–4, pp. 229–256, May 1992.
[92]
C. J. Maddison, A. Mnih, and Y. W. Teh, “The concrete distribution: A continuous relaxation of discrete random variables,” 2016,.
[93]
L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D: Nonlinear Phenomena, vol. 60, no. 1–4, pp. 259–268, 1992.
[94]
B. K. Horn and B. G. Schunck, “Determining optical flow,” Artif. Intell., vol. 17, no. 1/3, pp. 185–203, 1981.
[95]
S. Roth and M. J. Black, “Fields of experts: A framework for learning image priors,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2005, pp. 860–867.
[96]
Z. Tang, Y. Gao, L. Karlinsky, P. Sattigeri, R. Feris, and D. Metaxas, “Onlineaugment: Online data augmentation with less domain knowledge,” 2020,.
[97]
D. P. Kingma and M. Welling, “Auto-encoding variational Bayes,” 2013,.
[98]
Y. Gao, Z. Tang, M. Zhou, and D. Metaxas, “Enabling data diversity: Efficient automatic augmentation via regularized adversarial training,” Int. Conf. Inf. Process. Med. Imag., 2021, pp. 85–97.
[99]
T. Suzuki, “TeachAugment: Data augmentation optimization using teacher knowledge,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2022, pp. 10 904–10 914.
[100]
L. Sixt, B. Wild, and T. Landgraf, “RenderGAN: Generating realistic labeled data,” Front. Robot. AI, vol. 5, 2018, Art. no.
[101]
L. Sharan et al., “Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translation,” IEEE J. Biomed. Health Informat., vol. 26, no. 1, pp. 127–138, Jan. 2022.
[102]
K. Sun, F. Meng, and Y. Tian, “Multi-level wavelet-based network embedded with edge enhancement information for underwater image enhancement,” J. Mar. Sci. Eng., vol. 10, no. 7, 2022, Art. no. [Online]. Available: https://www.mdpi.com/2077–1312/10/7/884
[103]
T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, and T. Aila, “Training generative adversarial networks with limited data,” in Proc. Adv. Neural Inf. Process. Syst., 2020, pp. 12 104–12 114.
[104]
S. Zhao, Z. Liu, J. Lin, J.-Y. Zhu, and S. Han, “Differentiable augmentation for data-efficient GAN training,” in Proc. Conf. Neural Inf. Process. Syst., 2020, pp. 7559–7570.
[105]
N.-T. Tran, V.-H. Tran, N.-B. Nguyen, T.-K. Nguyen, and N.-M. Cheung, “On data augmentation for GAN training,” IEEE Trans. Image Process., vol. 30, pp. 1882–1897, 2021.
[106]
B. Zhao and H. Bilen, “Dataset condensation with differentiable siamese augmentation,” in Proc. Int. Conf. Mach. Learn., 2021, pp. 12 674–12 685.
[107]
J. Shi, P. Zhang, N. Zhang, H. Ghazzai, and Y. Massoud, “Dissolving is amplifying: Towards fine-grained anomaly detection,” 2023,.
[108]
J. Lemley, S. Bazrafkan, and P. Corcoran, “Smart augmentation learning an optimal data augmentation strategy,” IEEE Access, vol. 5, pp. 5858–5869, 2017.
[109]
G. Benton, M. Finzi, P. Izmailov, and A. G. Wilson, “Learning invariances in neural networks from training data,” in Proc. Adv. Neural Inf. Process. Syst., 2020, pp. 17 605–17 616.
[110]
C. Rommel, T. Moreau, and A. Gramfort, “Deep invariant networks with differentiable augmentation layers,” 2022,.
[111]
T. Suzuki and I. Sato, “Adversarial transformations for semi-supervised learning,” in Proc. AAAI Conf. Artif. Intell., 2020, pp. 5916–5923.
[112]
A. J. Ratner, H. Ehrenberg, Z. Hussain, J. Dunnmon, and C. Ré, “Learning to compose domain-specific transformations for data augmentation,” in Proc. Adv. Neural Inf. Process. Syst., 2017, pp. 3239–3249.
[113]
M. Raissi, P. Perdikaris, and G. Karniadakis, “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” J. Comput. Phys., vol. 378, pp. 686–707, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0021999118307125
[114]
A. Dubois et al., “Untrained physically informed neural network for image reconstruction of magnetic field sources,” 2022,.
[115]
A. Shrivastava, A. Gupta, and R. Girshick, “Training region-based object detectors with online hard example mining,” in Proc. Conf. Comput. Vis. Pattern Recognit., 2016, pp. 761–769.
[116]
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, “AutoAugment: Learning augmentation strategies from data,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2019, pp. 113–123.
[117]
K. Tian, C. Lin, M. Sun, L. Zhou, J. Yan, and W. Ouyang, “Improving auto-augment via augmentation-wise weight sharing,” in Proc. Adv. Neural Inf. Process. Syst., 2020, pp. 19 088–19 098.
[118]
B. Colson, P. Marcotte, and G. Savard, “An overview of bilevel optimization,” Ann. Operations Res., vol. 153, pp. 235–256, 2007.
[119]
D. Ho, E. Liang, X. Chen, I. Stoica, and P. Abbeel, “Population based augmentation: Efficient learning of augmentation policy schedules,” in Proc. Int. Conf. Mach. Learn., 2019, pp. 2731–2741.
[120]
H. Liu, K. Simonyan, and Y. Yang, “Darts: Differentiable architecture search,” in Proc. Int. Conf. Learn. Representations, 2019.
[121]
A. Liu, Z. Huang, Z. Huang, and N. Wang, “Direct differentiable augmentation search,” in Proc. IEEE/CVF Int. Conf. Comput. Vis., 2021, pp. 12 219–12 228.
[122]
S. Mounsaveng, I. Laradji, I. B. Ayed, D. Vazquez, and M. Pedersoli, “Learning data augmentation with online bilevel optimization for image classification,” in Proc. IEEE/CVF Winter Conf. Appl. Comput. Vis., 2021, pp. 1691–1700.
[123]
S. Lim, I. Kim, T. Kim, C. Kim, and S. Kim, “Fast autoaugment,” in Proc. Adv. Neural Inf. Process. Syst., 2019, pp. 6665–6675.
[124]
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in Proc. 34th Int. Conf. Mach. Learn., 2017, pp. 214–223.
[125]
Y. Zheng, Z. Zhang, S. Yan, and M. Zhang, “Deep autoaugmentation,” in Proc. Int. Conf. Learn. Representations, 2022.
[126]
X. Xu, H. Zhao, and P. Torr, “Universal adaptive data augmentation,” 2022,.
[127]
A. Fawzi, H. Samulowitz, D. Turaga, and P. Frossard, “Adaptive data augmentation for image classification,” in Proc. IEEE Int. Conf. Image Process., 2016, pp. 3688–3692.
[128]
N. Miao et al., “Instance-specific augmentation: Capturing local invariances,” in Proc. 40th Int. Conf. Mach. Learn., 2023.
[129]
V. Chinbat and S.-H. Bae, “GA3N: Generative adversarial autoaugment network,” Pattern Recognit., vol. 127, 2022, Art. no. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0031320322001182
[130]
T.-H. Cheung and D.-Y. Yeung, “AdaAug: Learning class-and instance-adaptive data augmentation policies,” in Proc. Int. Conf. Learn. Representations, 2021.
[131]
A. Xiao, B. Shen, J. Tian, and Z. Hu, “Differentiable RandAugment: Learning selecting weights and magnitude distributions of image transformations,” IEEE Trans. Image Process., vol. 32, pp. 2413–2427, 2023.
[132]
K. Bi, C. Hu, L. Xie, X. Chen, L. Wei, and Q. Tian, “Stabilizing darts with amended gradient estimation on architectural parameters,” 2019,.
[133]
R. Balestriero, L. Bottou, and Y. LeCun, “The effects of regularization and data augmentation are class dependent,” 2022,.
[134]
J. Cohen, E. Rosenfeld, and Z. Kolter, “Certified adversarial robustness via randomized smoothing,” in Proc. Int. Conf. Mach. Learn., 2019, pp. 1310–1320.
[135]
B. Li, C. Chen, W. Wang, and L. Carin, “Certified adversarial robustness with additive noise,” in Proc. Adv. Neural Inf. Process. Syst., 2019, pp. 9464–9474.
[136]
C. Xie, J. Wang, Z. Zhang, Z. Ren, and A. Yuille, “Mitigating adversarial effects through randomization,” 2017,.
[137]
A. Sablayrolles, M. Douze, C. Schmid, Y. Ollivier, and H. Jégou, “White-box vs black-box: Bayes optimal strategies for membership inference,” in Proc. Int. Conf. Mach. Learn., 2019, pp. 5558–5567.
[138]
T. Geffner and J. Domke, “A rule for gradient estimator selection, with an application to variational inference,” in Proc. Int. Conf. Artif. Intell. Statist., 2020, pp. 1803–1812.
[139]
S. Ravuri and O. Vinyals, “Seeing is not necessarily believing: Limitations of BigGANs for data augmentation,” in Proc. Int. Conf. Learn. Representations, 2019.
[140]
S. Wu, H. Zhang, G. Valiant, and C. Ré, “On the generalization effects of linear transformations in data augmentation,” in Proc. Int. Conf. Mach. Learn., 2020, pp. 10 410–10 420.
Index Terms
- Differentiable Image Data Augmentation and Its Applications: A Survey
Index terms have been assigned to the content through auto-classification.
Recommendations
Globally Convergent Inexact Generalized Newton Method for First-Order Differentiable Optimization Problems
Motivated by the method of Martinez and Qi (Ref. 1), we propose in this paper a globally convergent inexact generalized Newton method to solve unconstrained optimization problems in which the objective functions have Lipschitz continuous gradient ...
Newton's Method for B-Differentiable Equations
In this paper, we extend the classical Newton method for solving continuously differentiable systems of nonlinear equations to B-differentiable systems. Such B-differentiable systems of equations provide a unified framework for the nonlinear ...
An unconstrained differentiable penalty method for implicit complementarity problems
Dedicated to the memory of Professor Xiaoling Sun 1963–2014In this paper, we introduce an unconstrained differentiable penalty method for solving implicit complementarity problems, which has an exponential convergence rate under the assumption of a uniform ξ-P-function. Instead of solving the unconstrained ...
Comments
Information & Contributors
Information
Published In
0162-8828 © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
Publisher
IEEE Computer Society
United States
Publication History
Published: 07 November 2023
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 10 Oct 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in