{"list":"other","total":53,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1812.04948","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","created":"2018-12-12","cited_by_count":6013},{"id":"1611.07004","title":"Image-to-Image Translation with Conditional Adversarial Networks","created":"2016-11-21","cited_by_count":4527},{"id":"2012.09841","title":"Taming Transformers for High-Resolution Image Synthesis","created":"2020-12-17","cited_by_count":3253},{"id":"1710.10196","title":"Progressive Growing of GANs for Improved Quality, Stability, and Variation","created":"2017-10-27","cited_by_count":2988},{"id":"1603.08155","title":"Perceptual Losses for Real-Time Style Transfer and Super-Resolution","created":"2016-03-27","cited_by_count":2694},{"id":"1811.12231","title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","created":"2018-11-29","cited_by_count":1432},{"id":"1604.07379","title":"Context Encoders: Feature Learning by Inpainting","created":"2016-04-25","cited_by_count":1428},{"id":"1711.11585","title":"High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs","created":"2017-11-30","cited_by_count":1179},{"id":"1511.07122","title":"Multi-Scale Context Aggregation by Dilated Convolutions","created":"2015-11-23","cited_by_count":1133},{"id":"1801.04406","title":"Which Training Methods for GANs do actually Converge?","created":"2018-01-13","cited_by_count":679},{"id":"1806.03589","title":"Free-Form Image Inpainting with Gated Convolution","created":"2018-06-10","cited_by_count":594},{"id":"1801.07892","title":"Generative Image Inpainting with Contextual Attention","created":"2018-01-24","cited_by_count":580},{"id":"1412.7062","title":"Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs","created":"2014-12-22","cited_by_count":554},{"id":"1701.04128","title":"Understanding the Effective Receptive Field in Deep Convolutional Neural Networks","created":"2017-01-15","cited_by_count":553},{"id":"1804.07723","title":"Image Inpainting for Irregular Holes Using Partial Convolutions","created":"2018-04-20","cited_by_count":505},{"id":"2105.03824","title":"FNet: Mixing Tokens with Fourier Transforms","created":"2021-05-09","cited_by_count":347},{"id":"2107.00645","title":"Global Filter Networks for Image Classification","created":"2021-07-01","cited_by_count":260},{"id":"1711.09404","title":"Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients","created":"2017-11-26","cited_by_count":236},{"id":"2103.10428","title":"Large Scale Image Completion via Co-Modulated Generative Adversarial Networks","created":"2021-03-18","cited_by_count":220},{"id":"1901.00212","title":"EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning","created":"2019-01-01","cited_by_count":163},{"id":"1903.04227","title":"Pluralistic Image Completion","created":"2019-03-11","cited_by_count":162},{"id":"1607.07539","title":"Semantic Image Inpainting with Deep Generative Models","created":"2016-07-26","cited_by_count":157},{"id":"2005.09704","title":"Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting","created":"2020-05-19","cited_by_count":124},{"id":"1908.03852","title":"StructureFlow: Image Inpainting via Structure-aware Appearance Flow","created":"2019-08-11","cited_by_count":118},{"id":"2008.03737","title":"Recurrent Feature Reasoning for Image Inpainting","created":"2020-08-09","cited_by_count":107},{"id":"2007.06929","title":"Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations","created":"2020-07-14","cited_by_count":102},{"id":"1905.12384","title":"Coherent Semantic Attention for Image Inpainting","created":"2019-05-29","cited_by_count":97},{"id":"1904.07475","title":"Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting","created":"2019-04-16","cited_by_count":94},{"id":"1611.09969","title":"High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis","created":"2016-11-30","cited_by_count":90},{"id":"1901.05945","title":"Foreground-aware Image Inpainting","created":"2019-01-17","cited_by_count":90},{"id":"2104.01431","title":"Aggregated Contextual Transformations for High-Resolution Image Inpainting","created":"2021-04-03","cited_by_count":89},{"id":"1801.09392","title":"Shift-Net: Image Inpainting via Deep Feature Rearrangement","created":"2018-01-29","cited_by_count":87},{"id":"1810.08771","title":"Image Inpainting via Generative Multi-column Convolutional Neural Networks","created":"2018-10-20","cited_by_count":81},{"id":"2005.11742","title":"High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling","created":"2020-05-24","cited_by_count":79},{"id":"1909.00968","title":"Image Inpainting with Learnable Bidirectional Attention Maps","created":"2019-09-03","cited_by_count":64},{"id":"1805.03356","title":"SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting","created":"2018-05-09","cited_by_count":58},{"id":"1911.10375","title":"Region Normalization for Image Inpainting","created":"2019-11-23","cited_by_count":54},{"id":"1711.08590","title":"Contextual-based Image Inpainting: Infer, Match, and Translate","created":"2017-11-23","cited_by_count":50},{"id":"2002.04170","title":"Learning to Incorporate Structure Knowledge for Image Inpainting","created":"2020-02-11","cited_by_count":37},{"id":"2104.13743","title":"Image Inpainting by End-to-End Cascaded Refinement with Mask Awareness","created":"2021-04-28","cited_by_count":35},{"id":"1905.09010","title":"PEPSI++: Fast and Lightweight Network for Image Inpainting","created":"2019-05-22","cited_by_count":24},{"id":"1907.10478","title":"Progressive Image Inpainting with Full-Resolution Residual Network","created":"2019-07-24","cited_by_count":19},{"id":"1909.12507","title":"Region-wise Generative Adversarial ImageInpainting for Large Missing Areas","created":"2019-09-27","cited_by_count":18},{"id":"2002.02609","title":"Image Fine-grained Inpainting","created":"2020-02-07","cited_by_count":16},{"id":"1904.03076","title":"SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks","created":"2019-04-05","cited_by_count":13},{"id":"2011.01077","title":"Image Inpainting with Learnable Feature Imputation","created":"2020-11-02","cited_by_count":5}],"next_cursor":null}