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We introduce Neural Representation of Distribution (NeRD) technique, a module for convolutional neural networks (CNNs) that can estimate the feature distribution by optimizing an underlying function mapping image coordinates to the feature distribution.
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26
1945
Earlier work this paper cites.
Liu, R., Jia, J.: Reducing boundary artifacts in image deconvolution. In: 2008 15th IEEE International Conference on Image Processing. pp. 505–508. IEEE (2008)
2008
Earlier work this paper cites.
Bengio, Y., Mesnil, G., Dauphin, Y., Rifai, S.: Better mixing via deep representations. In: International conference on machine learning. pp. 552–560. PMLR (2013)
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
Lemke, C., Budka, M., Gabrys, B.: Metalearning: a survey of trends and technologies. Artificial intelligence review 44
2015
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.
Noh, H., Hongsuck Seo, P., Han, B.: Image question answering using convolutional neural network with dynamic parameter prediction. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 30–38 (2016)
2016
Earlier work this paper cites.
Upchurch, P., Gardner, J., Pleiss, G., Pless, R., Snavely, N., Bala, K., Weinberger, K.: Deep feature interpolation for image content changes. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7064–7073 (2017)
2017
Earlier work this paper cites.
Cheng, H.T., Chao, C.H., Dong, J.D., Wen, H.K., Liu, T.L., Sun, M.: Cube padding for weakly-supervised saliency prediction in 360 videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1420–1429 (2018)
2018
Earlier work this paper cites.
Hu, R., Dollár, P., He, K., Darrell, T., Girshick, R.: Learning to segment every thing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4233–4241 (2018)
2018
Earlier work this paper cites.
Hu, X., Mu, H., Zhang, X., Wang, Z., Tan, T., Sun, J.: Meta-sr: A magnification-arbitrary network for super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1575–1584 (2019)
2019
Earlier work this paper cites.
Innamorati, C., Ritschel, T., Weyrich, T., Mitra, N.J.: Learning on the edge: Investigating boundary filters in cnns. International Journal of Computer Vision pp. 1–10 (2019)
2019
Cited alongside, same era.
Islam, M.A., Jia, S., Bruce, N.D.: How much position information do convolutional neural networks encode? In: International Conference on Learning Representations (2019)
2019
Cited alongside, same era.
Kervadec, H., Bouchtiba, J., Desrosiers, C., Granger, E., Dolz, J., Ayed, I.B.: Boundary loss for highly unbalanced segmentation. In: International conference on medical imaging with deep learning. pp. 285–296. PMLR (2019)
2019
Cited alongside, same era.
Kuijf, H.J., Biesbroek, J.M., De Bresser, J., Heinen, R., Andermatt, S., Bento, M., Berseth, M., Belyaev, M., Cardoso, M.J., Casamitjana, A., et al.: Standardized assessment of automatic segmentation of white matter hyperintensities and results of the wmh segmentation challenge. IEEE transactions on medical imaging 38
2019
2020
Later among the works it cites.
Jiang, C., Sud, A., Makadia, A., Huang, J., Nießner, M., Funkhouser, T., et al.: Local implicit grid representations for 3d scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6001–6010 (2020)
2020
Later among the works it cites.
Kayhan, O.S., Gemert, J.C.v.: On translation invariance in cnns: Convolutional layers can exploit absolute spatial location. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14274–14285 (2020)
2020
Later among the works it cites.
La Rosa, F., Abdulkadir, A., Fartaria, M.J., Rahmanzadeh, R., Lu, P.J., Galbusera, R., Barakovic, M., Thiran, J.P., Granziera, C., Cuadra, M.B.: Multiple sclerosis cortical and wm lesion segmentation at 3t mri: a deep learning method based on flair and mp2rage. NeuroImage: Clinical 27
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Cited alongside, same era.
Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: Deepsdf: Learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 165–174 (2019)
2019
Cited alongside, same era.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems. pp. 8024–8035 (2019)
2019
Cited alongside, same era.
Schubert, S., Neubert, P., Pöschmann, J., Pretzel, P.: Circular convolutional neural networks for panoramic images and laser data. In: 2019 IEEE Intelligent Vehicles Symposium (IV). pp. 653–660. IEEE (2019)
2019
Cited alongside, same era.
Zhang, H., Valcarcel, A.M., Bakshi, R., Chu, R., Bagnato, F., Shinohara, R.T., Hett, K., Oguz, I.: Multiple sclerosis lesion segmentation with tiramisu and 2.5 d stacked slices. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 338–346. Springer (2019)
2019
Cited alongside, same era.
Zhang, R.: Making convolutional networks shift-invariant again. In: International Conference on Machine Learning. pp. 7324–7334 (2019)
2019
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
Zhang, J., Liu, Z., Zhang, S., Zhang, H., Spincemaille, P., Nguyen, T.D., Sabuncu, M.R., Wang, Y.: Fidelity imposed network edit (fine) for solving ill-posed image reconstruction. NeuroImage 211
2020
Later among the works it cites.
Zhang, J., Zhang, H., Sabuncu, M., Spincemaille, P., Nguyen, T., Wang, Y.: Bayesian learning of probabilistic dipole inversion for quantitative susceptibility mapping. In: Medical Imaging with Deep Learning. pp. 892–902. PMLR (2020)
2020
Later among the works it cites.
Zill, D.G.: Advanced engineering mathematics. Jones & Bartlett Publishers (2020)
2020
Later among the works it cites.
Xiong, Z., Xia, Q., Hu, Z., Huang, N., Bian, C., Zheng, Y., Vesal, S., Ravikumar, N., Maier, A., Yang, X., et al.: A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Medical Image Analysis 67
2021
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