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Cost-effective depth and infrared sensors as alternatives to usual RGB sensors are now a reality, and have some advantages over RGB in domains like autonomous navigation and remote sensing.
Unsupervised classifiers, mutual information and’phantom targets’
Bridle, J.S., Heading, A.J., MacKay, D.J.: · 1992
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Cycada: Cycle-consistent adversarial domain adaptation
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Multi-spectral sift for scene category recognition
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A theoretical analysis of metric hypothesis transfer learning
Perrot, M., Habrard, A.: · 2015
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Sun rgb-d: A rgb-d scene understanding benchmark suite
Song, S., Lichtenberg, S.P., Xiao, J.: · 2015
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Unsupervised domain adaptation by backpropagation
Ganin, Y., Lempitsky, V.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Cross modal distillation for supervision transfer
Gupta, S., Hoffman, J., Malik, J.: · 2016
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Cross-modal adaptation for rgb-d detection
Hoffman, J., Gupta, S., Leong, J., Guadarrama, S., Darrell, T.: · 2016
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Structure selective depth superresolution for rgb-d cameras
Kim, Y., Ham, B., Oh, C., Sohn, K.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Salimans, T., Kingma, D.P.: · 2016
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Rethinking the inception architecture for computer vision
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Adversarial discriminative domain adaptation
Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A., Valpola, H.: · 2017
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Deep stereo confidence prediction for depth estimation
Kim, S., Min, D., Ham, B., Kim, S., Sohn, K.: · 2017
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Cross and learn: Cross-modal self-supervision
Sayed, N., Brattoli, B., Ommer, B.: · 2018
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Zero-shot deep domain adaptation
Peng, K.C., Wu, Z., Ernst, J.: · 2018
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Unsupervised robust domain adaptation without source data
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Deep monocular depth estimation via integration of global and local predictions
Kim, Y., Jung, H., Min, D., Sohn, K.: · 2018
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Cross-modal knowledge distillation for action recognition
Thoker, F.M., Gall, J.: · 2019
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Knowledge as priors: Cross-modal knowledge generalization for datasets without superior knowledge
Zhao, L., Peng, X., Chen, Y., Kapadia, M., Metaxas, D.N.: · 2020
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Progressive domain adaptation for object detection
Hsu, H.K., Yao, C.H., Tsai, Y.H., Hung, W.C., Tseng, H.Y., Singh, M., Yang, M.H.: · 2020
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Domain adaptive semantic segmentation using weak labels
Paul, S., Tsai, Y.H., Schulter, S., Roy-Chowdhury, A.K., Chandraker, M.: · 2020
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Casting a bait for offline and online source-free domain adaptation
Yang, S., Wang, Y., van de Weijer, J., Herranz, L., Jui, S.: · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Yin, H., Molchanov, P., Alvarez, J.M., Li, Z., Mallya, A., Hoiem, D., Jha, N.K., Kautz, J.: · 2020
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Garcia, N.C., Bargal, S.A., Ablavsky, V., Morerio, P., Murino, V., Sclaroff, S.: · 2019
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Translate-to-recognize networks for rgb-d scene recognition
Du, D., Wang, L., Wang, H., Zhao, K., Wu, G.: · 2019
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Centroid based concept learning for rgb-d indoor scene classification
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Moment matching for multi-source domain adaptation
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: · 2019
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Larger norm more transferable: An adaptive feature norm approach for unsupervised domain adaptation
Xu, R., Li, G., Yang, J., Lin, L.: · 2019
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Unsupervised multi-source domain adaptation without access to source data
Ahmed, S.M., Raychaudhuri, D.S., Paul, S., Oymak, S., Roy-Chowdhury, A.K.: · 2021
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Learning an augmented rgb representation with cross-modal knowledge distillation for action detection
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Cross-modal knowledge distillation method for automatic cued speech recognition
Wang, J., Tang, Z., Li, X., Yu, M., Fang, Q., Liu, L.: · 2021
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Translate to adapt: Rgb-d scene recognition across domains
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Generalized source-free domain adaptation
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Exploiting the intrinsic neighborhood structure for source-free domain adaptation
Yang, S., Wang, Y., van de Weijer, J., Herranz, L., Jui, S.: · 2021
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Source data-absent unsupervised domain adaptation through hypothesis transfer and labeling transfer
Liang, J., Hu, D., Wang, Y., He, R., Feng, J.: · 2021
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Zero-shot deep domain adaptation with common representation learning
Kutbi, M., Peng, K.C., Wu, Z.: · 2021
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Deep monocular depth estimation leveraging a large-scale outdoor stereo dataset
Cho, J., Min, D., Kim, Y., Sohn, K.: · 2021
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Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: · 2030
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