Fetching the paper…
Reading the bibliography…
In recent years, there have been attempts to increase the kernel size of Convolutional Neural Nets (CNNs) to mimic the global receptive field of Vision Transformers' (ViTs) self-attention blocks.
Daubechies, I.: Ten lectures on wavelets. SIAM (1992)
1992
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
Vanhoucke, V.: Learning visual representations at scale. ICLR invited talk 1
2014
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q.: Deep networks with stochastic depth. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14. pp. 646–661. Springer (2016)
2016
Earlier work this paper cites.
Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. Advances in neural information processing systems 29
2016
Earlier work this paper cites.
maintainers, T., contributors: Torchvision: Pytorch’s computer vision library. https://github.com/pytorch/vision (2016)
2016
Earlier work this paper cites.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2818–2826 (2016)
2016
Earlier work this paper cites.
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1251–1258 (2017)
2017
Earlier work this paper cites.
Duan, Y., Liu, F., Jiao, L., Zhao, P., Zhang, L.: SAR image segmentation based on convolutional-wavelet neural network and markov random field. Pattern Recognition 64
2017
Earlier work this paper cites.
Guo, T., Seyed Mousavi, H., Huu Vu, T., Monga, V.: Deep wavelet prediction for image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops. pp. 104–113 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Huang, H., He, R., Sun, Z., Tan, T.: Wavelet-srnet: A wavelet-based cnn for multi-scale face super resolution. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 1689–1697 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Wang, M., Liu, B., Foroosh, H.: Factorized convolutional neural networks. In: Proceedings of the IEEE International Conference on Computer Vision Workshops. pp. 545–553 (2017)
2017
Earlier work this paper cites.
Contributors, M.: MMDetection: Openmmlab detection toolbox and benchmark. https://github.com/open-mmlab/mmdetection (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Liu, P., Zhang, H., Zhang, K., Lin, L., Zuo, W.: Multi-level wavelet-cnn for image restoration. In: Conference on Computer Vision and Pattern Recognition workshops. pp. 773–782 (2018)
2018
Earlier work this paper cites.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4510–4520 (2018)
2018
Earlier work this paper cites.
Williams, T., Li, R.: Wavelet pooling for convolutional neural networks. In: International Conference on Learning Representations (2018)
2018
Cited alongside, same era.
Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified perceptual parsing for scene understanding. In: Proceedings of the European conference on computer vision (ECCV). pp. 418–434 (2018)
2018
Cited alongside, same era.
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. In: International Conference on Learning Representations (2018)
2018
Cited alongside, same era.
Cai, Z., Vasconcelos, N.: Cascade r-cnn: high quality object detection and instance segmentation. IEEE transactions on pattern analysis and machine intelligence 43
2019
Cited alongside, same era.
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., Song, D., Steinhardt, J., Gilmer, J.: The many faces of robustness: A critical analysis of out-of-distribution generalization. In: Proceedings of the IEEE international conference on computer vision (2021)
2021
Later among the works it cites.
Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., Song, D.: Natural adversarial examples. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2021)
2021
Later among the works it cites.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10012–10022 (2021)
2021
Later among the works it cites.
Mintun, E., Kirillov, A., Xie, S.: On interaction between augmentations and corruptions in natural corruption robustness. In: Advances in Neural Information Processing Systems (2021)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Chen, Y., Fan, H., Xu, B., Yan, Z., Kalantidis, Y., Rohrbach, M., Yan, S., Feng, J.: Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 3435–3444 (2019)
2019
Cited alongside, same era.
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. In: International Conference on Learning Representations (2019)
2019
Cited alongside, same era.
Haber, E., Lensink, K., Treister, E., Ruthotto, L.: IMEXnet a forward stable deep neural network. In: Proceedings of the 36th International Conference on Machine Learning (2019)
2019
Cited alongside, same era.
Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. In: Proceedings of the International Conference on Learning Representations (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. pp. 6105–6114. PMLR (2019)
2019
Cited alongside, same era.
Wang, H., Ge, S., Lipton, Z., Xing, E.P.: Learning robust global representations by penalizing local predictive power. In: Advances in Neural Information Processing Systems. pp. 10506–10518 (2019)
2019
Cited alongside, same era.
Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: Regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 6023–6032 (2019)
2019
Cited alongside, same era.
2021
Later among the works it cites.
Naseer, M.M., Ranasinghe, K., Khan, S.H., Hayat, M., Shahbaz Khan, F., Yang, M.H.: Intriguing properties of vision transformers. Advances in Neural Information Processing Systems 34
2021
Later among the works it cites.
Rao, Y., Zhao, W., Zhu, Z., Lu, J., Zhou, J.: Global filter networks for image classification. Advances in neural information processing systems 34
2021
Later among the works it cites.
2021
Later among the works it cites.
Wang, T., Lu, C., Sun, Y., Yang, M., Liu, C., Ou, C.: Automatic ecg classification using continuous wavelet transform and convolutional neural network. Entropy (2021)
2021
Later among the works it cites.
Alaba, S.Y., Ball, J.E.: Wcnn3d: Wavelet convolutional neural network-based 3d object detection for autonomous driving. Sensors 22
2022
Later among the works it cites.
Ding, X., Zhang, X., Han, J., Ding, G.: Scaling up your kernels to 31x31: Revisiting large kernel design in cnns. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11963–11975 (2022)
2022
Later among the works it cites.
Finder, S.E., Zohav, Y., Ashkenazi, M., Treister, E.: Wavelet feature maps compression for image-to-image cnns. In: Advances in Neural Information Processing Systems (2022)
2022
Later among the works it cites.
Guth, F., Coste, S., De Bortoli, V., Mallat, S.: Wavelet score-based generative modeling. In: Advances in Neural Information Processing Systems (2022)
2022
Later among the works it cites.
Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A convnet for the 2020s. In: Conference on Computer Vision and Pattern Recognition (2022)
2022
Later among the works it cites.
Park, N., Kim, S.: How do vision transformers work? arXiv preprint arXiv:2202.06709 (2022)
2022
Later among the works it cites.
Ronen, M., Finder, S.E., Freifeld, O.: Deepdpm: Deep clustering with an unknown number of clusters. In: Conference on Computer Vision and Pattern Recognition (2022)
2022
Later among the works it cites.
Guo, M.H., Lu, C.Z., Liu, Z.N., Cheng, M.M., Hu, S.M.: Visual attention network. Computational Visual Media 9
2023
Later among the works it cites.
Li, Z., Ortega Caro, J., Rusak, E., Brendel, W., Bethge, M., Anselmi, F., Patel, A.B., Tolias, A.S., Pitkow, X.: Robust deep learning object recognition models rely on low frequency information in natural images. PLOS Computational Biology (2023)
2023
Later among the works it cites.
Liu, S., Chen, T., Chen, X., Chen, X., Xiao, Q., Wu, B., Pechenizkiy, M., Mocanu, D., Wang, Z.: More convnets in the 2020s: Scaling up kernels beyond 51x51 using sparsity. In: International Conference on Learning Representations (2023)
2023
Later among the works it cites.
Phung, H., Dao, Q., Tran, A.: Wavelet diffusion models are fast and scalable image generators. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10199–10208 (2023)
2023
Later among the works it cites.
Saragadam, V., LeJeune, D., Tan, J., Balakrishnan, G., Veeraraghavan, A., Baraniuk, R.G.: Wire: Wavelet implicit neural representations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18507–18516 (2023)
2023
Later among the works it cites.
Trockman, A., Kolter, J.Z.: Patches are all you need? Transactions on Machine Learning Research (2023)
2023
Later among the works it cites.
Gavrikov, P., Keuper, J.: Can biases in imagenet models explain generalization? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22184–22194 (2024)
2024
Closest in time.
Grabinski, J., Keuper, J., Keuper, M.: As large as it gets – studying infinitely large convolutions via neural implicit frequency filters. Transactions on Machine Learning Research (2024)
2024
Closest in time.