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Generative models have recently undergone significant advancement due to the diffusion models.
Extracting and composing robust features with denoising autoencoders
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A · 2008
Earlier work this paper cites.
Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, P. K. and Fergus, R · 2012
Earlier work this paper cites.
Indoor segmentation and support inference from rgbd images
Silberman, N., Hoiem, D., Kohli, P., and Fergus, R · 2012
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Lee, D.-H. et al · 2013
Earlier work this paper cites.
Holistic scene understanding for 3d object detection with rgbd cameras
Lin, D., Fidler, S., and Urtasun, R · 2013
Earlier work this paper cites.
Learning with pseudo-ensembles
Bachman, P., Alsharif, O., and Precup, D · 2014
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
Eigen, D., Puhrsch, C., and Fergus, R · 2014
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs
Li, B., Shen, C., Dai, Y., Van Den Hengel, A., and He, M · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2015
Earlier work this paper cites.
Designing deep networks for surface normal estimation
Wang, X., Fouhey, D., and Gupta, A · 2015
Earlier work this paper cites.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., and Xiao, J · 2015
Earlier work this paper cites.
Unified depth prediction and intrinsic image decomposition from a single image via joint convolutional neural fields
Kim, S., Park, K., Sohn, K., and Lin, S · 2016
Earlier work this paper cites.
Deeper depth prediction with fully convolutional residual networks
Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., and Navab, N · 2016
Earlier work this paper cites.
Temporal ensembling for semi-supervised learning
Laine, S. and Aila, T · 2016
Earlier work this paper cites.
Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Sajjadi, M., Javanmardi, M., and Tasdizen, T · 2016
Earlier work this paper cites.
Cnn-slam: Real-time dense monocular slam with learned depth prediction
Tateno, K., Tombari, F., Laina, I., and Navab, N · 2017
Earlier work this paper cites.
Demon: Depth and motion network for learning monocular stereo
Ummenhofer, B., Zhou, H., Uhrig, J., Mayer, N., Ilg, E., Dosovitskiy, A., and Brox, T · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Cited alongside, same era.
Deep ordinal regression network for monocular depth estimation
Fu, H., Gong, M., Wang, C., Batmanghelich, K., and Tao, D · 2018
Cited alongside, same era.
Open3D: A modern library for 3D data processing
Zhou, Q.-Y., Park, J., and Koltun, V · 2018
Cited alongside, same era.
Digging into self-supervised monocular depth estimation
Godard, C., Mac Aodha, O., Firman, M., and Brostow, G. J · 2019
Cited alongside, same era.
From big to small: Multi-scale local planar guidance for monocular depth estimation
Lee, J. H., Han, M.-K., Ko, D. W., and Suh, I. H · 2019
Cited alongside, same era.
Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving
Vision transformers for dense prediction
Ranftl, R., Bochkovskiy, A., and Koltun, V · 2021
Later among the works it cites.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2021
Later among the works it cites.
Decoder denoising pretraining for semantic segmentation
Asiedu, E. B., Kornblith, S., Chen, T., Parmar, N., Minderer, M., and Norouzi, M · 2022
Closest in time.
ediffi: Text-to-image diffusion models with an ensemble of expert denoisers
Balaji, Y., Nah, S., Huang, X., Vahdat, A., Song, J., Kreis, K., Aittala, M., Aila, T., Laine, S., Catanzaro, B., et al · 2022
Closest in time.
Label-efficient semantic segmentation with diffusion models
Baranchuk, D., Voynov, A., Rubachev, I., Khrulkov, V., and Babenko, A · 2022
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Wang, Y., Chao, W.-L., Garg, D., Hariharan, B., Campbell, M., and Weinberger, K. Q · 2019
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
Cited alongside, same era.
Taming transformers for high-resolution image synthesis, 2020
Esser, P., Rombach, R., and Ommer, B · 2020
Cited alongside, same era.
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Sohn, K., Berthelot, D., Carlini, N., Zhang, Z., Zhang, H., Raffel, C. A., Cubuk, E. D., Kurakin, A., and Li, C.-L · 2020
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
Cited alongside, same era.
Farid, H · 2022
Closest in time.
Diffusion models as plug-and-play priors
Graikos, A., Malkin, N., Jojic, N., and Samaras, D · 2022
Closest in time.
Vector quantized diffusion model for text-to-image synthesis
Gu, S., Chen, D., Bao, J., Wen, F., Zhang, B., Chen, D., Yuan, L., and Guo, B · 2022
Closest in time.
Improving sample quality of diffusion models using self-attention guidance
Hong, S., Lee, G., Jang, W., and Kim, S · 2022
Closest in time.
Denoising diffusion restoration models
Kawar, B., Elad, M., Ermon, S., and Song, J · 2022
Closest in time.
Li, Z., Chen, Z., Liu, X., and Jiang, J · 2022
Closest in time.
Magic3d: High-resolution text-to-3d content creation
Lin, C.-H., Gao, J., Tang, L., Takikawa, T., Zeng, X., Huang, X., Kreis, K., Fidler, S., Liu, M.-Y., and Lin, T.-Y · 2022
Closest in time.
Dreamfusion: Text-to-3d using 2d diffusion
Poole, B., Jain, A., Barron, J. T., and Mildenhall, B · 2022
Closest in time.
Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
Closest in time.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Closest in time.
Palette: Image-to-image diffusion models
Saharia, C., Chan, W., Chang, H., Lee, C., Ho, J., Salimans, T., Fleet, D., and Norouzi, M · 2022
Closest in time.
Midms: Matching interleaved diffusion models for exemplar-based image translation
Seo, J., Lee, G., Cho, S., Lee, J., and Kim, S · 2022
Closest in time.
3d-aware indoor scene synthesis with depth priors
Shi, Z., Shen, Y., Zhu, J., Yeung, D.-Y., and Chen, Q · 2022
Closest in time.
Disentangled3d: Learning a 3d generative model with disentangled geometry and appearance from monocular images
Tewari, A., Pan, X., Fried, O., Agrawala, M., Theobalt, C., et al · 2022
Closest in time.
Diffusers: State-of-the-art diffusion models
von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M., and Wolf, T · 2022
Closest in time.