{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/concatenated-skip-connection/papers/27","list_of":"/method/concatenated-skip-connection","method":"Concatenated Skip Connection","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":27,"pages_in_order":34,"rows_per_page":100,"rows":[2601,2700],"of":3337,"counts":{"archive_papers_tagged":3337,"with_a_code_link":1339,"where_syntology_ran_a_sample":224,"not_listed_spam_title":0,"listed":3337,"listed_where_code_ran":224,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":190,"every_run_a_failure_of_syntologys_instrument":34,"listed_with_a_run_with_no_instrument_failure":190,"listed_every_run_a_failure_of_syntologys_instrument":34,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/concatenated-skip-connection","prev":"/method/concatenated-skip-connection/papers/26","next":"/method/concatenated-skip-connection/papers/28","papers":[{"paper":"/paper/vqvc-one-shot-voice-conversion-by-vector","slug":"vqvc-one-shot-voice-conversion-by-vector","title":"VQVC+: One-Shot Voice Conversion by Vector Quantization and U-Net architecture","date":"2020-06-07","arxiv_id":"2006.04154","n_code_links":1,"syntology":null},{"paper":"/paper/deep-octree-based-cnns-with-output-guided","slug":"deep-octree-based-cnns-with-output-guided","title":"Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion","date":"2020-06-06","arxiv_id":"2006.03762","n_code_links":1,"syntology":null},{"paper":null,"slug":"msdu-net-a-multi-scale-dilated-u-net-for-blur","title":"MSDU-net: A Multi-Scale Dilated U-net for Blur Detection","date":"2020-06-05","arxiv_id":"2006.03182","n_code_links":0,"syntology":null},{"paper":null,"slug":"segmentation-of-surgical-instruments-for","title":"Segmentation of Surgical Instruments for Minimally-Invasive Robot-Assisted Procedures Using Generative Deep Neural Networks","date":"2020-06-05","arxiv_id":"2006.03486","n_code_links":0,"syntology":null},{"paper":null,"slug":"ct-based-covid-19-triage-deep-multitask","title":"CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification","date":"2020-06-02","arxiv_id":"2006.01441","n_code_links":0,"syntology":null},{"paper":null,"slug":"dilated-u-net-based-approach-for-multichannel","title":"Dilated U-net based approach for multichannel speech enhancement from First-Order Ambisonics recordings","date":"2020-06-02","arxiv_id":"2006.01708","n_code_links":0,"syntology":null},{"paper":"/paper/a-u-net-based-discriminator-for-generative-1","slug":"a-u-net-based-discriminator-for-generative-1","title":"A U-Net Based Discriminator for Generative Adversarial Networks","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/a2-link-recognizing-disguised-faces-via","slug":"a2-link-recognizing-disguised-faces-via","title":"A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain Knowledge","date":"2020-06-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"bwcnn-blink-to-word-a-real-time-convolutional","title":"BWCNN: Blink to Word, a Real-Time Convolutional Neural Network Approach","date":"2020-06-01","arxiv_id":"2006.01232","n_code_links":0,"syntology":null},{"paper":null,"slug":"disparity-aware-domain-adaptation-in-stereo","title":"Disparity-Aware Domain Adaptation in Stereo Image Restoration","date":"2020-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/dc-unet-rethinking-the-u-net-architecture","slug":"dc-unet-rethinking-the-u-net-architecture","title":"DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation","date":"2020-05-31","arxiv_id":"2006.00414","n_code_links":4,"syntology":{"ran":5,"of":5,"n_ran_checked":3,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["AngeLouCN/DC-UNet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"end-to-end-change-detection-for-high","title":"End-to-End Change Detection for High Resolution Drone Images with GAN Architecture","date":"2020-05-31","arxiv_id":"2006.00467","n_code_links":0,"syntology":null},{"paper":null,"slug":"advanced-single-image-resolution-upsurging","title":"Advanced Single Image Resolution Upsurging Using a Generative Adversarial Network","date":"2020-05-30","arxiv_id":"2006.00186","n_code_links":0,"syntology":null},{"paper":"/paper/reconstructing-undersampled-photoacoustic","slug":"reconstructing-undersampled-photoacoustic","title":"Reconstructing undersampled photoacoustic microscopy images using deep learning","date":"2020-05-30","arxiv_id":"2006.00251","n_code_links":2,"syntology":null},{"paper":null,"slug":"automatic-segmentation-of-the-pulmonary-lobes","title":"Automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function","date":"2020-05-29","arxiv_id":"2006.00083","n_code_links":0,"syntology":null},{"paper":null,"slug":"snr-based-teachers-student-technique-for","title":"SNR-Based Teachers-Student Technique for Speech Enhancement","date":"2020-05-29","arxiv_id":"2005.14441","n_code_links":0,"syntology":null},{"paper":null,"slug":"wavesnet-wavelet-integrated-deep-networks-for","title":"WaveSNet: Wavelet Integrated Deep Networks for Image Segmentation","date":"2020-05-29","arxiv_id":"2005.14461","n_code_links":0,"syntology":null},{"paper":null,"slug":"knowledge-driven-learning-via-experts-consult","title":"Multimodal Feature Fusion and Knowledge-Driven Learning via Experts Consult for Thyroid Nodule Classification","date":"2020-05-28","arxiv_id":"2005.14117","n_code_links":0,"syntology":null},{"paper":null,"slug":"concurrent-segmentation-and-object-detection","title":"Concurrent Segmentation and Object Detection CNNs for Aircraft Detection and Identification in Satellite Images","date":"2020-05-27","arxiv_id":"2005.13215","n_code_links":0,"syntology":null},{"paper":null,"slug":"tackling-the-problem-of-large-deformations-in","title":"Tackling the Problem of Large Deformations in Deep Learning Based Medical Image Registration Using Displacement Embeddings","date":"2020-05-27","arxiv_id":"2005.13338","n_code_links":0,"syntology":null},{"paper":"/paper/towards-the-infeasibility-of-membership","slug":"towards-the-infeasibility-of-membership","title":"On the Difficulty of Membership Inference Attacks","date":"2020-05-27","arxiv_id":"2005.13702","n_code_links":1,"syntology":null},{"paper":"/paper/identification-of-crystal-symmetry-from-noisy","slug":"identification-of-crystal-symmetry-from-noisy","title":"Identification of Crystal Symmetry from Noisy Diffraction Patterns by A Shape Analysis and Deep Learning","date":"2020-05-26","arxiv_id":"2005.12476","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-efficiency-of-deep-learning-algorithms","title":"The efficiency of deep learning algorithms for detecting anatomical reference points on radiological images of the head profile","date":"2020-05-25","arxiv_id":"2005.12110","n_code_links":0,"syntology":null},{"paper":null,"slug":"coronavirus-comparing-covid-19-sars-and-mers","title":"Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-Ray Images","date":"2020-05-23","arxiv_id":"2005.11524","n_code_links":0,"syntology":null},{"paper":"/paper/gleason-grading-of-histology-prostate-images","slug":"gleason-grading-of-histology-prostate-images","title":"Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net","date":"2020-05-22","arxiv_id":"2005.11368","n_code_links":1,"syntology":null},{"paper":null,"slug":"hf-unet-learning-hierarchically-inter-task","title":"HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation","date":"2020-05-21","arxiv_id":"2005.10439","n_code_links":0,"syntology":null},{"paper":null,"slug":"data-consistent-ct-reconstruction-from","title":"Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images","date":"2020-05-20","arxiv_id":"2005.10034","n_code_links":0,"syntology":null},{"paper":null,"slug":"map-generation-from-large-scale-incomplete","title":"Map Generation from Large Scale Incomplete and Inaccurate Data Labels","date":"2020-05-20","arxiv_id":"2005.10053","n_code_links":0,"syntology":null},{"paper":"/paper/reducing-overlearning-through-disentangled","slug":"reducing-overlearning-through-disentangled","title":"Reducing Overlearning through Disentangled Representations by Suppressing Unknown Tasks","date":"2020-05-20","arxiv_id":"2005.10220","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-segment-clustered-amoeboid-cells","title":"Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection","date":"2020-05-19","arxiv_id":"2005.09372","n_code_links":0,"syntology":null},{"paper":null,"slug":"medical-image-generation-using-generative","title":"Medical Image Generation using Generative Adversarial Networks","date":"2020-05-19","arxiv_id":"2005.10687","n_code_links":0,"syntology":null},{"paper":null,"slug":"tropical-and-extratropical-cyclone-detection","title":"Tropical and Extratropical Cyclone Detection Using Deep Learning","date":"2020-05-18","arxiv_id":"2005.09056","n_code_links":0,"syntology":null},{"paper":null,"slug":"tripletunet-multi-task-u-net-with-online","title":"MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT Prostate Segmentation via Online Sampling","date":"2020-05-15","arxiv_id":"2005.07462","n_code_links":0,"syntology":null},{"paper":null,"slug":"context-learning-for-bone-shadow-exclusion-in","title":"Context Learning for Bone Shadow Exclusion in CheXNet Accuracy Improvement","date":"2020-05-13","arxiv_id":"2005.06189","n_code_links":0,"syntology":null},{"paper":null,"slug":"train-and-deploy-an-image-classifier-for","title":"Train and Deploy an Image Classifier for Disaster Response","date":"2020-05-12","arxiv_id":"2005.05495","n_code_links":0,"syntology":null},{"paper":null,"slug":"very-high-resolution-land-cover-mapping-of","title":"Very High Resolution Land Cover Mapping of Urban Areas at Global Scale with Convolutional Neural Networks","date":"2020-05-12","arxiv_id":"2005.05652","n_code_links":0,"syntology":null},{"paper":"/paper/ecg-delnet-delineation-of-ambulatory","slug":"ecg-delnet-delineation-of-ambulatory","title":"ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks","date":"2020-05-11","arxiv_id":"2005.05236","n_code_links":1,"syntology":null},{"paper":"/paper/iunets-fully-invertible-u-nets-with-learnable","slug":"iunets-fully-invertible-u-nets-with-learnable","title":"iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling","date":"2020-05-11","arxiv_id":"2005.05220","n_code_links":2,"syntology":{"ran":12,"of":14,"n_ran_checked":11,"n_instrument":1,"unverified":2,"pointer_only":1,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["cetmann/iunets"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"medical-image-segmentation-using-a-u-net-type","title":"Medical Image Segmentation Using a U-Net type of Architecture","date":"2020-05-11","arxiv_id":"2005.05218","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-context-based-non-local-entropy","title":"Learning Context-Based Non-local Entropy Modeling for Image Compression","date":"2020-05-10","arxiv_id":"2005.04661","n_code_links":0,"syntology":null},{"paper":"/paper/a-sim2real-deep-learning-approach-for-the","slug":"a-sim2real-deep-learning-approach-for-the","title":"A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird's Eye View","date":"2020-05-08","arxiv_id":"2005.04078","n_code_links":2,"syntology":{"ran":6,"of":6,"n_ran_checked":6,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ika-rwth-aachen/Cam2BEV"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"comparison-and-benchmarking-of-ai-models-and","title":"Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices","date":"2020-05-07","arxiv_id":"2005.05085","n_code_links":0,"syntology":null},{"paper":"/paper/wavelet-integrated-cnns-for-noise-robust","slug":"wavelet-integrated-cnns-for-noise-robust","title":"Wavelet Integrated CNNs for Noise-Robust Image Classification","date":"2020-05-07","arxiv_id":"2005.03337","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":2,"phrase":"0 ran · 2 unverified","official":{"repos":["LiQiufu/WaveCNet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/deephist-differentiable-joint-and-color","slug":"deephist-differentiable-joint-and-color","title":"DeepHist: Differentiable Joint and Color Histogram Layers for Image-to-Image Translation","date":"2020-05-06","arxiv_id":"2005.03995","n_code_links":1,"syntology":null},{"paper":null,"slug":"adaptive-low-rank-factorization-to-regularize","title":"Adaptive Low-Rank Factorization to regularize shallow and deep neural networks","date":"2020-05-05","arxiv_id":"2005.01995","n_code_links":0,"syntology":null},{"paper":"/paper/video-frame-interpolation-via-residue","slug":"video-frame-interpolation-via-residue","title":"Video Frame Interpolation via Residue Refinement","date":"2020-05-04","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"projection-inpainting-using-partial","title":"Projection Inpainting Using Partial Convolution for Metal Artifact Reduction","date":"2020-05-02","arxiv_id":"2005.00762","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-cascade-network-for-detecting-covid-19","title":"A cascade network for Detecting COVID-19 using chest x-rays","date":"2020-05-01","arxiv_id":"2005.01468","n_code_links":0,"syntology":null},{"paper":null,"slug":"deeply-cascaded-u-net-for-multi-task-image","title":"Deeply Cascaded U-Net for Multi-Task Image Processing","date":"2020-05-01","arxiv_id":"2005.00225","n_code_links":0,"syntology":null},{"paper":null,"slug":"feedback-u-net-for-cell-image-segmentation","title":"Feedback U-net for Cell Image Segmentation","date":"2020-04-30","arxiv_id":"2004.14581","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-time-frequency-reconstruction-by","title":"Robust Time-Frequency Reconstruction by Learning Structured Sparsity","date":"2020-04-30","arxiv_id":"2004.14820","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-machine-learning-approach-to-develop-a","title":"Deep Machine Learning Approach to Develop a New Asphalt Pavement Condition Index","date":"2020-04-28","arxiv_id":"2004.13314","n_code_links":0,"syntology":null},{"paper":"/paper/dru-net-an-efficient-deep-convolutional","slug":"dru-net-an-efficient-deep-convolutional","title":"DRU-net: An Efficient Deep Convolutional Neural Network for Medical Image Segmentation","date":"2020-04-28","arxiv_id":"2004.13453","n_code_links":1,"syntology":null},{"paper":"/paper/fu-net-multi-class-image-segmentation-using","slug":"fu-net-multi-class-image-segmentation-using","title":"FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net","date":"2020-04-28","arxiv_id":"2004.13470","n_code_links":1,"syntology":null},{"paper":"/paper/multi-scale-boosted-dehazing-network-with","slug":"multi-scale-boosted-dehazing-network-with","title":"Multi-Scale Boosted Dehazing Network with Dense Feature Fusion","date":"2020-04-28","arxiv_id":"2004.13388","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-scoping-review-of-transfer-learning","title":"A scoping review of transfer learning research on medical image analysis using ImageNet","date":"2020-04-27","arxiv_id":"2004.13175","n_code_links":0,"syntology":null},{"paper":null,"slug":"compact-retail-shelf-segmentation-for-mobile","title":"Compact retail shelf segmentation for mobile deployment","date":"2020-04-27","arxiv_id":"2004.13094","n_code_links":0,"syntology":null},{"paper":null,"slug":"or-unet-an-optimized-robust-residual-u-net","title":"OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Images","date":"2020-04-27","arxiv_id":"2004.12668","n_code_links":0,"syntology":null},{"paper":"/paper/deepseg-deep-neural-network-framework-for","slug":"deepseg-deep-neural-network-framework-for","title":"DeepSeg: Deep Neural Network Framework for Automatic Brain Tumor Segmentation using Magnetic Resonance FLAIR Images","date":"2020-04-26","arxiv_id":"2004.12333","n_code_links":1,"syntology":null},{"paper":"/paper/yolov4-optimal-speed-and-accuracy-of-object","slug":"yolov4-optimal-speed-and-accuracy-of-object","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","date":"2020-04-23","arxiv_id":"2004.10934","n_code_links":223,"syntology":{"ran":142,"of":184,"n_ran_checked":133,"n_instrument":9,"unverified":42,"pointer_only":21,"phrase":"142 ran (of which 0 constructed an object rather than computing a result; 133 with no instrument failure: 6 honoured, 0 violated, 127 with no contract checked; 9 where Syntology's instrument failed) · 42 unverified","official":{"repos":["AlexeyAB/darknet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"automatic-detection-of-coronavirus-disease-1","title":"Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach","date":"2020-04-22","arxiv_id":"2004.10641","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-polyp-segmentation-using","title":"Automatic Polyp Segmentation Using Convolutional Neural Networks","date":"2020-04-22","arxiv_id":"2004.10792","n_code_links":0,"syntology":null},{"paper":"/paper/ml-lbm-machine-learning-aided-flow-simulation","slug":"ml-lbm-machine-learning-aided-flow-simulation","title":"ML-LBM: Machine Learning Aided Flow Simulation in Porous Media","date":"2020-04-22","arxiv_id":"2004.11675","n_code_links":1,"syntology":null},{"paper":"/paper/yoga-82-a-new-dataset-for-fine-grained","slug":"yoga-82-a-new-dataset-for-fine-grained","title":"Yoga-82: A New Dataset for Fine-grained Classification of Human Poses","date":"2020-04-22","arxiv_id":"2004.10362","n_code_links":1,"syntology":null},{"paper":null,"slug":"spatio-spectral-deep-learning-methods-for-in","title":"Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection","date":"2020-04-21","arxiv_id":"2004.10159","n_code_links":0,"syntology":null},{"paper":"/paper/torchgpipe-on-the-fly-pipeline-parallelism","slug":"torchgpipe-on-the-fly-pipeline-parallelism","title":"torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models","date":"2020-04-21","arxiv_id":"2004.09910","n_code_links":3,"syntology":null},{"paper":"/paper/deep-covid-predicting-covid-19-from-chest-x","slug":"deep-covid-predicting-covid-19-from-chest-x","title":"Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning","date":"2020-04-20","arxiv_id":"2004.09363","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-feature-extraction-for-3d","slug":"self-supervised-feature-extraction-for-3d","title":"Self-Supervised Feature Extraction for 3D Axon Segmentation","date":"2020-04-20","arxiv_id":"2004.09629","n_code_links":1,"syntology":null},{"paper":null,"slug":"litedensenet-a-lightweight-network-for","title":"LiteDenseNet: A Lightweight Network for Hyperspectral Image Classification","date":"2020-04-17","arxiv_id":"2004.08112","n_code_links":0,"syntology":null},{"paper":"/paper/multi-scale-supervised-3d-u-net-for-kidneys","slug":"multi-scale-supervised-3d-u-net-for-kidneys","title":"Multi-Scale Supervised 3D U-Net for Kidneys and Kidney Tumor Segmentation","date":"2020-04-17","arxiv_id":"2004.08108","n_code_links":1,"syntology":null},{"paper":null,"slug":"caggnet-crossing-aggregation-network-for","title":"CAggNet: Crossing Aggregation Network for Medical Image Segmentation","date":"2020-04-16","arxiv_id":"2004.08237","n_code_links":0,"syntology":null},{"paper":null,"slug":"analysis-of-scoliosis-from-spinal-x-ray","title":"Analysis of Scoliosis From Spinal X-Ray Images","date":"2020-04-15","arxiv_id":"2004.06887","n_code_links":0,"syntology":null},{"paper":null,"slug":"image-segmentation-using-hybrid","title":"Image Segmentation Using Hybrid Representations","date":"2020-04-15","arxiv_id":"2004.07071","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-automatic-covid-19-ct-segmentation-based","title":"An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism","date":"2020-04-14","arxiv_id":"2004.06673","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-cell-counting-with-neural","slug":"exploring-cell-counting-with-neural","title":"Systematically designing better instance counting models on cell images with Neural Arithmetic Logic Units","date":"2020-04-14","arxiv_id":"2004.06674","n_code_links":1,"syntology":null},{"paper":null,"slug":"line-art-correlation-matching-network-for","title":"Line Art Correlation Matching Feature Transfer Network for Automatic Animation Colorization","date":"2020-04-14","arxiv_id":"2004.06718","n_code_links":0,"syntology":null},{"paper":"/paper/res-cr-net-a-residual-network-with-a-novel","slug":"res-cr-net-a-residual-network-with-a-novel","title":"Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy images","date":"2020-04-14","arxiv_id":"2004.08246","n_code_links":1,"syntology":null},{"paper":null,"slug":"transfer-learning-with-deep-convolutional","title":"Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray","date":"2020-04-14","arxiv_id":"2004.06578","n_code_links":0,"syntology":null},{"paper":"/paper/analysis-on-deeplabv3-performance-for","slug":"analysis-on-deeplabv3-performance-for","title":"Analysis on DeepLabV3+ Performance for Automatic Steel Defects Detection","date":"2020-04-09","arxiv_id":"2004.04822","n_code_links":1,"syntology":null},{"paper":null,"slug":"capsules-for-biomedical-image-segmentation","title":"Capsules for Biomedical Image Segmentation","date":"2020-04-09","arxiv_id":"2004.04736","n_code_links":0,"syntology":null},{"paper":null,"slug":"centermask-single-shot-instance-segmentation","title":"CenterMask: single shot instance segmentation with point representation","date":"2020-04-09","arxiv_id":"2004.04446","n_code_links":0,"syntology":null},{"paper":"/paper/lung-nodule-detection-and-classification-from","slug":"lung-nodule-detection-and-classification-from","title":"Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning","date":"2020-04-08","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"bayesian-aggregation-improves-traditional","title":"Bayesian aggregation improves traditional single image crop classification approaches","date":"2020-04-07","arxiv_id":"2004.03468","n_code_links":0,"syntology":null},{"paper":"/paper/breastscreening-on-the-use-of-multi-modality","slug":"breastscreening-on-the-use-of-multi-modality","title":"BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis","date":"2020-04-07","arxiv_id":"2004.03500","n_code_links":8,"syntology":null},{"paper":"/paper/channel-attention-residual-u-net-for-retinal","slug":"channel-attention-residual-u-net-for-retinal","title":"Channel Attention Residual U-Net for Retinal Vessel Segmentation","date":"2020-04-07","arxiv_id":"2004.03702","n_code_links":2,"syntology":null},{"paper":"/paper/dense-residual-network-for-retinal-vessel","slug":"dense-residual-network-for-retinal-vessel","title":"Dense Residual Network for Retinal Vessel Segmentation","date":"2020-04-07","arxiv_id":"2004.03697","n_code_links":2,"syntology":null},{"paper":"/paper/sa-unet-spatial-attention-u-net-for-retinal","slug":"sa-unet-spatial-attention-u-net-for-retinal","title":"SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation","date":"2020-04-07","arxiv_id":"2004.03696","n_code_links":4,"syntology":null},{"paper":"/paper/toward-fine-grained-facial-expression","slug":"toward-fine-grained-facial-expression","title":"Toward Fine-grained Facial Expression Manipulation","date":"2020-04-07","arxiv_id":"2004.03132","n_code_links":1,"syntology":null},{"paper":null,"slug":"u-net-using-stacked-dilated-convolutions-for","title":"U-Net Using Stacked Dilated Convolutions for Medical Image Segmentation","date":"2020-04-07","arxiv_id":"2004.03466","n_code_links":0,"syntology":null},{"paper":"/paper/a-learning-framework-for-n-bit-quantized","slug":"a-learning-framework-for-n-bit-quantized","title":"A Learning Framework for n-bit Quantized Neural Networks toward FPGAs","date":"2020-04-06","arxiv_id":"2004.02396","n_code_links":1,"syntology":null},{"paper":null,"slug":"condenseunet-a-memory-efficient-condensely","title":"CondenseUNet: A Memory-Efficient Condensely-Connected Architecture for Bi-ventricular Blood Pool and Myocardium Segmentation","date":"2020-04-05","arxiv_id":"2004.02249","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimization-of-image-embeddings-for-few-shot","title":"Optimization of Image Embeddings for Few Shot Learning","date":"2020-04-04","arxiv_id":"2004.02034","n_code_links":0,"syntology":null},{"paper":"/paper/a-fast-fully-octave-convolutional-neural","slug":"a-fast-fully-octave-convolutional-neural","title":"A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation","date":"2020-04-03","arxiv_id":"2004.01317","n_code_links":1,"syntology":null},{"paper":"/paper/cell-segmentation-and-tracking-using-distance","slug":"cell-segmentation-and-tracking-using-distance","title":"Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy","date":"2020-04-03","arxiv_id":"2004.01486","n_code_links":1,"syntology":null},{"paper":null,"slug":"attention-based-assisted-excitation-for","title":"Attention-based Assisted Excitation for Salient Object Detection","date":"2020-03-31","arxiv_id":"2003.14194","n_code_links":0,"syntology":null},{"paper":null,"slug":"characterizing-speech-adversarial-examples","title":"Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement","date":"2020-03-31","arxiv_id":"2003.13917","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-oracle-attention-for-high-fidelity","title":"Learning Oracle Attention for High-fidelity Face Completion","date":"2020-03-31","arxiv_id":"2003.13903","n_code_links":0,"syntology":null},{"paper":null,"slug":"radiologist-level-stroke-classification-on","title":"Radiologist-level stroke classification on non-contrast CT scans with Deep U-Net","date":"2020-03-31","arxiv_id":"2003.14287","n_code_links":0,"syntology":null},{"paper":null,"slug":"weakly-supervised-land-classification-for","title":"Weakly-supervised land classification for coastal zone based on deep convolutional neural networks by incorporating dual-polarimetric characteristics into training dataset","date":"2020-03-30","arxiv_id":"2003.13648","n_code_links":0,"syntology":null},{"paper":null,"slug":"fastidious-attention-network-for-navel-orange","title":"Fastidious Attention Network for Navel Orange Segmentation","date":"2020-03-26","arxiv_id":"2003.11734","n_code_links":0,"syntology":null}],"record_sha256":"3f78a5e6f09d36b0482fd00c96df759e72c92cde265f445380db5c6606d16fdd","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}