Охота на электроовец. Большая книга искусственного интеллекта - Сергей Сергеевич Марков
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Zhang H., Xu T., Li H., Zhang S., Wang X., Huang X., Metaxas D. (2018). StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks // https://arxiv.org/abs/1710.10916
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Wah C., Branson S., Welinder P., Perona P., Belongie S. (2011). The Caltech-UCSD Birds-200-2011 Dataset. Technical Report CNS-TR2011-001, California Institute of Technology // http://www.vision.caltech.edu/visipedia/papers/CUB_200_2011.pdf
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Zhang H., Xu T., Li H., Zhang S., Wang X., Huang X., Metaxas D. (2017). StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks // https://arxiv.org/abs/1710.10916
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Sun W., Chen Z. (2019). Learned Image Downscaling for Upscaling using Content Adaptive Resampler // https://arxiv.org/abs/1907.12904
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Lim B., Son S., Kim H., Nah S., Lee K. M. (2017). Enhanced Deep Residual Networks for Single Image Super-Resolution // https://arxiv.org/abs/1707.02921
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Ma C., Rao Y., Cheng Y., Chen C., Lu J., Zhou J. (2020). Structure-Preserving Super Resolution with Gradient Guidance // https://arxiv.org/abs/2003.13081
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Niu B., Wen W., Ren W., Zhang X., Yang L., Wang S., Zhang K., Cao X., Shen H. (2020). Single Image Super-Resolution via a Holistic Attention Network // https://arxiv.org/abs/2008.08767
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Kawulok M., Benecki P., Piechaczek S., Hrynczenko K., Kostrzewa D., Nalepa J. (2019). Deep Learning for Multiple-Image Super-Resolution // https://arxiv.org/abs/1903.00440
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Zhu M., Pan P., Chen W., Yang Y. (2019). DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis // https://arxiv.org/abs/1904.01310
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Xu T., Zhang P., Huang Q., Zhang H., Gan Z., Huang X., He X. (2017). AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks // https://arxiv.org/abs/1711.10485
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Liang J., Pei W., Lu F. (2019). CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis // https://paperswithcode.com/paper/cpgan-full-spectrum-content-parsing
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Parmar N., Vaswani A., Uszkoreit J., Kaiser Ł., Shazeer N., Ku A., Tran D. (2018). Image Transformer // https://arxiv.org/abs/1802.05751
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Wu B., Xu C., Dai X., Wan A., Zhang P., Yan Z., Tomizuka M., Gonzalez J., Keutzer K., Vajda P. (2020). Visual Transformers: Token-based Image Representation and Processing for Computer Vision // https://arxiv.org/abs/2006.03677
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Dosovitskiy A., Beyer L., Kolesnikov A., Weissenborn D., Zhai X., Unterthiner T., Dehghani M., Minderer M., Heigold G., Gelly S., Uszkoreit J., Houlsby N. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale // https://arxiv.org/abs/2010.11929
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Touvron H., Cord M., Douze M., Massa F., Sablayrolles A., Jégou H. (2020). Training data-efficient image transformers & distillation through attention // https://arxiv.org/abs/2012.12877
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Liu Z., Lin Y., Cao Y., Hu H., Wei Y., Zhang Z., Lin S., Guo B. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows // https://arxiv.org/abs/2103.14030
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Carion N., Massa F., Synnaeve G., Usunier N., Kirillov A., Zagoruyko S. (2020). End-to-end Object Detection with Transformers // https://ai.facebook.com/research/publications/end-to-end-object-detection-with-transformers
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Zhu X., Su W., Lu L., Li B., Wang X., Dai J. (2020). Deformable DETR: Deformable Transformers for End-to-End Object Detection // https://arxiv.org/abs/2010.04159
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Guo J., Han K., Wu H., Xu C., Tang Y., Xu C., Wang Y. (2021). CMT: Convolutional Neural Networks Meet Vision Transformers // https://arxiv.org/abs/2107.06263
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Wu H., Xiao B., Codella N., Liu M., Dai X., Yuan L., Zhang L. (2021). CvT: Introducing Convolutions to Vision Transformers // https://arxiv.org/abs/2103.15808
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Touvron H., Cord M., Sablayrolles A., Synnaeve G., Jégou H. (2021). Going deeper with Image Transformers // https://arxiv.org/abs/2103.17239
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Yuan K., Guo S., Liu Z., Zhou A., Yu F., Wu W. (2021). Incorporating Convolution Designs into Visual Transformers // https://arxiv.org/abs/2103.11816
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Chen M., Peng H., Fu J., Ling H. (2021). AutoFormer: Searching Transformers for Visual Recognition // https://arxiv.org/abs/2107.00651
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Han K., Xiao A., Wu E., Guo J., Xu C., Wang Y. (2021). Transformer in Transformer // https://arxiv.org/abs/2103.00112
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Wang Y., Huang R., Song S., Huang Z., Huang G. (2021). Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition // https://arxiv.org/abs/2105.15075
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Chen X., Hsieh C.-J., Gong B. (2021). When Vision Transformers Outperform ResNets without Pre-training or Strong Data Augmentations // https://arxiv.org/abs/2106.01548
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Dai Z., Liu H., Le Q. V., Tan M. (2021). CoAtNet: Marrying Convolution and Attention for All Data Sizes // https://arxiv.org/abs/2106.04803
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Liu Z., Hu H., Lin Y., Yao Z., Xie Z., Wei Y., Ning J., Cao Y., Zhang Z., Dong L., Wei F., Guo B. (2021). Swin Transformer V2: Scaling Up Capacity and Resolution // https://arxiv.org/abs/2111.09883
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Li Y., Wu C.-Y., Fan H., Mangalam K., Xiong B., Malik J., Feichtenhofer C. (2021). Improved Multiscale Vision Transformers for Classification and Detection // https://arxiv.org/abs/2112.01526
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Dong X., Bao J., Zhang T., Chen D., Zhang W., Yuan L., Chen D., Wen F., Yu N. (2021). PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers // https://arxiv.org/abs/2111.12710
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Wu S., Wu T., Tan H., Guo G. (2021). Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped Attention // https://arxiv.org/abs/2112.14000
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Liu Z., Mao H., Wu C.-Y., Feichtenhofer C., Darrell T., Xie S. (2022). A ConvNet for the 2020s // https://arxiv.org/abs/2201.03545
