{"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/convolution/papers/173","list_of":"/method/convolution","method":"Convolution","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":173,"pages_in_order":196,"rows_per_page":100,"rows":[17201,17300],"of":19586,"counts":{"archive_papers_tagged":19586,"with_a_code_link":8064,"where_syntology_ran_a_sample":1837,"not_listed_spam_title":0,"listed":19586,"listed_where_code_ran":1837,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1557,"every_run_a_failure_of_syntologys_instrument":280,"listed_with_a_run_with_no_instrument_failure":1557,"listed_every_run_a_failure_of_syntologys_instrument":280,"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/convolution","prev":"/method/convolution/papers/172","next":"/method/convolution/papers/174","papers":[{"paper":"/paper/sscnets-a-selective-sobel-convolution-based","slug":"sscnets-a-selective-sobel-convolution-based","title":"SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters","date":"2018-11-04","arxiv_id":"1811.01443","n_code_links":1,"syntology":null},{"paper":null,"slug":"auto-ml-deep-learning-for-rashi-scripts-ocr","title":"Auto-ML Deep Learning for Rashi Scripts OCR","date":"2018-11-03","arxiv_id":"1811.01290","n_code_links":0,"syntology":null},{"paper":null,"slug":"closed-loop-gan-for-continual-learning","title":"Closed-Loop Memory GAN for Continual Learning","date":"2018-11-03","arxiv_id":"1811.01146","n_code_links":0,"syntology":null},{"paper":"/paper/dunet-a-deformable-network-for-retinal-vessel","slug":"dunet-a-deformable-network-for-retinal-vessel","title":"DUNet: A deformable network for retinal vessel segmentation","date":"2018-11-03","arxiv_id":"1811.01206","n_code_links":0,"syntology":null},{"paper":"/paper/invertible-residual-networks","slug":"invertible-residual-networks","title":"Invertible Residual Networks","date":"2018-11-02","arxiv_id":"1811.00995","n_code_links":5,"syntology":{"ran":9,"of":9,"n_ran_checked":2,"n_instrument":7,"unverified":0,"pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 7 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"ischemic-stroke-lesion-segmentation-in-ct","title":"Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Pyramid Pooling and Focal Loss","date":"2018-11-02","arxiv_id":"1811.01085","n_code_links":0,"syntology":null},{"paper":null,"slug":"minimax-estimation-of-neural-net-distance","title":"Minimax Estimation of Neural Net Distance","date":"2018-11-02","arxiv_id":"1811.01054","n_code_links":0,"syntology":null},{"paper":"/paper/sdcnet-video-prediction-using-spatially","slug":"sdcnet-video-prediction-using-spatially","title":"SDCNet: Video Prediction Using Spatially-Displaced Convolution","date":"2018-11-02","arxiv_id":"1811.00684","n_code_links":2,"syntology":null},{"paper":"/paper/show-attend-and-read-a-simple-and-strong","slug":"show-attend-and-read-a-simple-and-strong","title":"Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition","date":"2018-11-02","arxiv_id":"1811.00751","n_code_links":8,"syntology":{"ran":7,"of":8,"n_ran_checked":7,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"bi-gans-st-for-perceptual-image-super","title":"Bi-GANs-ST for Perceptual Image Super-resolution","date":"2018-11-01","arxiv_id":"1811.00367","n_code_links":0,"syntology":null},{"paper":null,"slug":"dilated-densenets-for-relational-reasoning","title":"Dilated DenseNets for Relational Reasoning","date":"2018-11-01","arxiv_id":"1811.00410","n_code_links":0,"syntology":null},{"paper":null,"slug":"retrieve-and-re-rank-a-simple-and-effective","title":"Retrieve and Re-rank: A Simple and Effective IR Approach to Simple Question Answering over Knowledge Graphs","date":"2018-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/accelerating-stochastic-training-for-over","slug":"accelerating-stochastic-training-for-over","title":"Accelerating SGD with momentum for over-parameterized learning","date":"2018-10-31","arxiv_id":"1810.13395","n_code_links":1,"syntology":null},{"paper":null,"slug":"generating-photo-realistic-training-data-to","title":"Generating Photo-Realistic Training Data to Improve Face Recognition Accuracy","date":"2018-10-31","arxiv_id":"1811.00112","n_code_links":0,"syntology":null},{"paper":"/paper/mixture-density-generative-adversarial","slug":"mixture-density-generative-adversarial","title":"Mixture Density Generative Adversarial Networks","date":"2018-10-31","arxiv_id":"1811.00152","n_code_links":1,"syntology":null},{"paper":null,"slug":"performance-assessment-of-the-deep-learning","title":"Performance assessment of the deep learning technologies in grading glaucoma severity","date":"2018-10-31","arxiv_id":"1810.13376","n_code_links":0,"syntology":null},{"paper":null,"slug":"splinenets-continuous-neural-decision-graphs","title":"SplineNets: Continuous Neural Decision Graphs","date":"2018-10-31","arxiv_id":"1810.13118","n_code_links":0,"syntology":null},{"paper":null,"slug":"structure-learning-of-deep-neural-networks","title":"Structure Learning of Deep Neural Networks with Q-Learning","date":"2018-10-31","arxiv_id":"1810.13155","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-effect-of-learning-strategy-versus","title":"The Effect of Learning Strategy versus Inherent Architecture Properties on the Ability of Convolutional Neural Networks to Develop Transformation Invariance","date":"2018-10-31","arxiv_id":"1810.13128","n_code_links":0,"syntology":null},{"paper":null,"slug":"visual-attention-network-for-low-dose-ct","title":"Visual Attention Network for Low Dose CT","date":"2018-10-31","arxiv_id":"1810.13059","n_code_links":0,"syntology":null},{"paper":"/paper/a-hybrid-frequency-domainimage-domain-deep","slug":"a-hybrid-frequency-domainimage-domain-deep","title":"A Hybrid Frequency-domain/Image-domain Deep Network for Magnetic Resonance Image Reconstruction","date":"2018-10-30","arxiv_id":"1810.12473","n_code_links":1,"syntology":null},{"paper":"/paper/dropblock-a-regularization-method-for","slug":"dropblock-a-regularization-method-for","title":"DropBlock: A regularization method for convolutional networks","date":"2018-10-30","arxiv_id":"1810.12890","n_code_links":10,"syntology":null},{"paper":"/paper/generative-adversarial-networks-for-unpaired","slug":"generative-adversarial-networks-for-unpaired","title":"Generative Adversarial Networks for Unpaired Voice Transformation on Impaired Speech","date":"2018-10-30","arxiv_id":"1810.12656","n_code_links":2,"syntology":null},{"paper":null,"slug":"waveform-generation-for-text-to-speech","title":"Waveform generation for text-to-speech synthesis using pitch-synchronous multi-scale generative adversarial networks","date":"2018-10-30","arxiv_id":"1810.12598","n_code_links":0,"syntology":null},{"paper":"/paper/deepsphere-efficient-spherical-convolutional","slug":"deepsphere-efficient-spherical-convolutional","title":"DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications","date":"2018-10-29","arxiv_id":"1810.12186","n_code_links":6,"syntology":null},{"paper":"/paper/few-shot-3d-multi-modal-medical-image","slug":"few-shot-3d-multi-modal-medical-image","title":"Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning","date":"2018-10-29","arxiv_id":"1810.12241","n_code_links":1,"syntology":{"ran":1,"of":6,"n_ran_checked":1,"n_instrument":0,"unverified":5,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["arnab39/FewShot_GAN-Unet3D"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"paper":"/paper/investigation-of-enhanced-tacotron-text-to","slug":"investigation-of-enhanced-tacotron-text-to","title":"Investigation of enhanced Tacotron text-to-speech synthesis systems with self-attention for pitch accent language","date":"2018-10-29","arxiv_id":"1810.11960","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-convex-duality-framework-for-gans","title":"A Convex Duality Framework for GANs","date":"2018-10-28","arxiv_id":"1810.11740","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatically-evolving-cnn-architectures","title":"Automatically Evolving CNN Architectures Based on Blocks","date":"2018-10-28","arxiv_id":"1810.11875","n_code_links":0,"syntology":null},{"paper":null,"slug":"object-tracking-in-hyperspectral-videos-with","title":"Object Tracking in Hyperspectral Videos with Convolutional Features and Kernelized Correlation Filter","date":"2018-10-28","arxiv_id":"1810.11819","n_code_links":0,"syntology":null},{"paper":"/paper/a-miniaturized-semantic-segmentation-method","slug":"a-miniaturized-semantic-segmentation-method","title":"A Miniaturized Semantic Segmentation Method for Remote Sensing Image","date":"2018-10-27","arxiv_id":"1810.11603","n_code_links":1,"syntology":null},{"paper":"/paper/a2-nets-double-attention-networks","slug":"a2-nets-double-attention-networks","title":"$A^2$-Nets: Double Attention Networks","date":"2018-10-27","arxiv_id":"1810.11579","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-supervised-gan-to-counter-forgetting","title":"Self-Supervised GAN to Counter Forgetting","date":"2018-10-27","arxiv_id":"1810.11598","n_code_links":0,"syntology":null},{"paper":null,"slug":"building-footprint-generation-using-improved","title":"Building Footprint Generation Using Improved Generative Adversarial Networks","date":"2018-10-26","arxiv_id":"1810.11224","n_code_links":0,"syntology":null},{"paper":null,"slug":"crystalgan-learning-to-discover","title":"CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks","date":"2018-10-26","arxiv_id":"1810.11203","n_code_links":0,"syntology":null},{"paper":null,"slug":"noise-sensitivity-of-local-descriptors-vs","title":"Noise Sensitivity of Local Descriptors vs ConvNets: An application to Facial Recognition","date":"2018-10-26","arxiv_id":"1810.11515","n_code_links":0,"syntology":null},{"paper":"/paper/spectrogram-channels-u-net-a-source","slug":"spectrogram-channels-u-net-a-source","title":"Spectrogram-channels u-net: a source separation model viewing each channel as the spectrogram of each source","date":"2018-10-26","arxiv_id":"1810.11520","n_code_links":1,"syntology":null},{"paper":null,"slug":"automating-generation-of-low-precision-deep","title":"Automating Generation of Low Precision Deep Learning Operators","date":"2018-10-25","arxiv_id":"1810.11066","n_code_links":0,"syntology":null},{"paper":"/paper/convolutional-deblurring-for-natural-imaging","slug":"convolutional-deblurring-for-natural-imaging","title":"Convolutional Deblurring for Natural Imaging","date":"2018-10-25","arxiv_id":"1810.10725","n_code_links":1,"syntology":null},{"paper":null,"slug":"gan-augmentation-augmenting-training-data","title":"GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks","date":"2018-10-25","arxiv_id":"1810.10863","n_code_links":0,"syntology":null},{"paper":null,"slug":"k-for-the-price-of-1-parameter-efficient","title":"K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning","date":"2018-10-25","arxiv_id":"1810.10703","n_code_links":0,"syntology":null},{"paper":"/paper/a-deep-learning-based-fashion-attributes","slug":"a-deep-learning-based-fashion-attributes","title":"A Deep-Learning-Based Fashion Attributes Detection Model","date":"2018-10-24","arxiv_id":"1810.10148","n_code_links":1,"syntology":null},{"paper":null,"slug":"mask-propagation-network-for-video-object","title":"Mask Propagation Network for Video Object Segmentation","date":"2018-10-24","arxiv_id":"1810.10289","n_code_links":0,"syntology":null},{"paper":null,"slug":"multistep-speed-prediction-on-traffic","title":"Multistep Speed Prediction on Traffic Networks: A Graph Convolutional Sequence-to-Sequence Learning Approach with Attention Mechanism","date":"2018-10-24","arxiv_id":"1810.10237","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-the-semantic-textual-similarity","title":"Predicting the Semantic Textual Similarity with Siamese CNN and LSTM","date":"2018-10-24","arxiv_id":"1810.10641","n_code_links":0,"syntology":null},{"paper":"/paper/spatiotemporal-cnns-for-pornography-detection","slug":"spatiotemporal-cnns-for-pornography-detection","title":"Spatiotemporal CNNs for Pornography Detection in Videos","date":"2018-10-24","arxiv_id":"1810.10519","n_code_links":1,"syntology":null},{"paper":"/paper/efficient-eligibility-traces-for-deep","slug":"efficient-eligibility-traces-for-deep","title":"Reconciling $λ$-Returns with Experience Replay","date":"2018-10-23","arxiv_id":"1810.09967","n_code_links":1,"syntology":null},{"paper":null,"slug":"hierarchical-approaches-for-reinforcement","title":"Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space","date":"2018-10-23","arxiv_id":"1810.09656","n_code_links":0,"syntology":null},{"paper":"/paper/reproducing-ambientgan-generative-models-from","slug":"reproducing-ambientgan-generative-models-from","title":"Reproducing AmbientGAN: Generative models from lossy measurements","date":"2018-10-23","arxiv_id":"1810.10108","n_code_links":1,"syntology":null},{"paper":null,"slug":"baseline-detection-in-historical-documents","title":"Baseline Detection in Historical Documents using Convolutional U-Nets","date":"2018-10-22","arxiv_id":"1810.09343","n_code_links":0,"syntology":null},{"paper":"/paper/can-we-gain-more-from-orthogonality","slug":"can-we-gain-more-from-orthogonality","title":"Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?","date":"2018-10-22","arxiv_id":"1810.09102","n_code_links":1,"syntology":null},{"paper":null,"slug":"dating-ancient-paintings-of-mogao-grottoes","title":"Dating Ancient Paintings of Mogao Grottoes Using Deeply Learnt Visual Codes","date":"2018-10-22","arxiv_id":"1810.09168","n_code_links":0,"syntology":null},{"paper":"/paper/graph-convolutional-reinforcement-learning","slug":"graph-convolutional-reinforcement-learning","title":"Graph Convolutional Reinforcement Learning","date":"2018-10-22","arxiv_id":"1810.09202","n_code_links":4,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["PKU-AI-Edge/DGN"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/single-image-haze-removal-using-a-generative","slug":"single-image-haze-removal-using-a-generative","title":"Single Image Haze Removal using a Generative Adversarial Network","date":"2018-10-22","arxiv_id":"1810.09479","n_code_links":2,"syntology":null},{"paper":null,"slug":"dermatologist-level-dermoscopy-skin-cancer","title":"Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms","date":"2018-10-21","arxiv_id":"1810.10348","n_code_links":0,"syntology":null},{"paper":null,"slug":"improved-techniques-for-gan-based-facial","title":"Improved Techniques for GAN based Facial Inpainting","date":"2018-10-20","arxiv_id":"1810.08774","n_code_links":0,"syntology":null},{"paper":null,"slug":"left-ventricle-segmentation-via-optical-flow","title":"Left Ventricle Segmentation via Optical-Flow-Net from Short-axis Cine MRI: Preserving the Temporal Coherence of Cardiac Motion","date":"2018-10-20","arxiv_id":"1810.08753","n_code_links":0,"syntology":null},{"paper":"/paper/on-extensions-of-clever-a-neural-network","slug":"on-extensions-of-clever-a-neural-network","title":"On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm","date":"2018-10-19","arxiv_id":"1810.08640","n_code_links":1,"syntology":null},{"paper":"/paper/a-novel-focal-tversky-loss-function-with","slug":"a-novel-focal-tversky-loss-function-with","title":"A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation","date":"2018-10-18","arxiv_id":"1810.07842","n_code_links":6,"syntology":null},{"paper":"/paper/deep-learning-methods-for-reynolds-averaged","slug":"deep-learning-methods-for-reynolds-averaged","title":"Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows","date":"2018-10-18","arxiv_id":"1810.08217","n_code_links":2,"syntology":{"ran":4,"of":4,"n_ran_checked":4,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["thunil/Deep-Flow-Prediction"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":"/paper/domain-adaptation-for-semantic-segmentation","slug":"domain-adaptation-for-semantic-segmentation","title":"Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training","date":"2018-10-18","arxiv_id":"1810.07911","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yzou2/CBST"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/implicit-dual-domain-convolutional-network","slug":"implicit-dual-domain-convolutional-network","title":"Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction","date":"2018-10-18","arxiv_id":"1810.08042","n_code_links":0,"syntology":null},{"paper":"/paper/mri-reconstruction-via-cascaded-channel-wise","slug":"mri-reconstruction-via-cascaded-channel-wise","title":"MRI Reconstruction via Cascaded Channel-wise Attention Network","date":"2018-10-18","arxiv_id":"1810.08229","n_code_links":1,"syntology":null},{"paper":null,"slug":"salience-biased-loss-for-object-detection-in","title":"Salience Biased Loss for Object Detection in Aerial Images","date":"2018-10-18","arxiv_id":"1810.08103","n_code_links":0,"syntology":null},{"paper":"/paper/laddernet-multi-path-networks-based-on-u-net","slug":"laddernet-multi-path-networks-based-on-u-net","title":"LadderNet: Multi-path networks based on U-Net for medical image segmentation","date":"2018-10-17","arxiv_id":"1810.07810","n_code_links":3,"syntology":null},{"paper":null,"slug":"learning-in-non-convex-games-with-an","title":"Learning in Non-convex Games with an Optimization Oracle","date":"2018-10-17","arxiv_id":"1810.07362","n_code_links":0,"syntology":null},{"paper":"/paper/recognizing-partial-biometric-patterns","slug":"recognizing-partial-biometric-patterns","title":"Recognizing Partial Biometric Patterns","date":"2018-10-17","arxiv_id":"1810.07399","n_code_links":1,"syntology":null},{"paper":"/paper/a-comparison-of-1-d-and-2-d-deep","slug":"a-comparison-of-1-d-and-2-d-deep","title":"A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification","date":"2018-10-16","arxiv_id":"1810.07088","n_code_links":1,"syntology":null},{"paper":null,"slug":"bottleneck-supervised-u-net-for-pixel-wise","title":"Bottleneck Supervised U-Net for Pixel-wise Liver and Tumor Segmentation","date":"2018-10-16","arxiv_id":"1810.10331","n_code_links":0,"syntology":null},{"paper":null,"slug":"dense-multi-path-u-net-for-ischemic-stroke","title":"Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities","date":"2018-10-16","arxiv_id":"1810.07003","n_code_links":0,"syntology":null},{"paper":"/paper/discriminator-rejection-sampling","slug":"discriminator-rejection-sampling","title":"Discriminator Rejection Sampling","date":"2018-10-16","arxiv_id":"1810.06758","n_code_links":1,"syntology":null},{"paper":null,"slug":"from-deep-to-physics-informed-learning-of","title":"From Deep to Physics-Informed Learning of Turbulence: Diagnostics","date":"2018-10-16","arxiv_id":"1810.07785","n_code_links":0,"syntology":null},{"paper":null,"slug":"metropolis-hastings-view-on-variational","title":"Metropolis-Hastings view on variational inference and adversarial training","date":"2018-10-16","arxiv_id":"1810.07151","n_code_links":0,"syntology":null},{"paper":null,"slug":"rotational-3d-texture-classification-using","title":"Rotational 3D Texture Classification Using Group Equivariant CNNs","date":"2018-10-16","arxiv_id":"1810.06889","n_code_links":0,"syntology":null},{"paper":null,"slug":"bshapenet-object-detection-and-instance","title":"BshapeNet: Object Detection and Instance Segmentation with Bounding Shape Masks","date":"2018-10-15","arxiv_id":"1810.10327","n_code_links":0,"syntology":null},{"paper":"/paper/refacing-reconstructing-anonymized-facial","slug":"refacing-reconstructing-anonymized-facial","title":"Refacing: reconstructing anonymized facial features using GANs","date":"2018-10-15","arxiv_id":"1810.06455","n_code_links":1,"syntology":null},{"paper":"/paper/successor-uncertainties-exploration-and","slug":"successor-uncertainties-exploration-and","title":"Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning","date":"2018-10-15","arxiv_id":"1810.06530","n_code_links":2,"syntology":{"ran":7,"of":7,"n_ran_checked":2,"n_instrument":5,"unverified":0,"pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"virtualization-of-tissue-staining-in-digital","title":"Virtualization of tissue staining in digital pathology using an unsupervised deep learning approach","date":"2018-10-15","arxiv_id":"1810.06415","n_code_links":0,"syntology":null},{"paper":null,"slug":"fine-grained-classification-of-cervical-cells","title":"Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks","date":"2018-10-14","arxiv_id":"1810.06058","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploiting-semantics-in-adversarial-training","title":"Exploiting Semantics in Adversarial Training for Image-Level Domain Adaptation","date":"2018-10-13","arxiv_id":"1810.05852","n_code_links":0,"syntology":null},{"paper":"/paper/point-cloud-gan","slug":"point-cloud-gan","title":"Point Cloud GAN","date":"2018-10-13","arxiv_id":"1810.05795","n_code_links":1,"syntology":null},{"paper":"/paper/dynamic-channel-pruning-feature-boosting-and","slug":"dynamic-channel-pruning-feature-boosting-and","title":"Dynamic Channel Pruning: Feature Boosting and Suppression","date":"2018-10-12","arxiv_id":"1810.05331","n_code_links":2,"syntology":{"ran":5,"of":7,"n_ran_checked":3,"n_instrument":2,"unverified":2,"pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["deep-fry/mayo"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/u-net-machine-reading-comprehension-with","slug":"u-net-machine-reading-comprehension-with","title":"U-Net: Machine Reading Comprehension with Unanswerable Questions","date":"2018-10-12","arxiv_id":"1810.06638","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-novel-domain-adaptation-framework-for","title":"A Novel Domain Adaptation Framework for Medical Image Segmentation","date":"2018-10-11","arxiv_id":"1810.05732","n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-text-generation-without","title":"Adversarial Text Generation Without Reinforcement Learning","date":"2018-10-11","arxiv_id":"1810.06640","n_code_links":0,"syntology":null},{"paper":null,"slug":"bayesian-deep-convolutional-networks-with","title":"Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes","date":"2018-10-11","arxiv_id":"1810.05148","n_code_links":0,"syntology":null},{"paper":"/paper/deep-bi-dense-networks-for-image-super","slug":"deep-bi-dense-networks-for-image-super","title":"Deep Bi-Dense Networks for Image Super-Resolution","date":"2018-10-11","arxiv_id":"1810.04873","n_code_links":1,"syntology":null},{"paper":"/paper/empowerment-driven-exploration-using-mutual","slug":"empowerment-driven-exploration-using-mutual","title":"Empowerment-driven Exploration using Mutual Information Estimation","date":"2018-10-11","arxiv_id":"1810.05533","n_code_links":1,"syntology":null},{"paper":null,"slug":"mdgan-boosting-anomaly-detection-using-multi","title":"MDGAN: Boosting Anomaly Detection Using \\\\Multi-Discriminator Generative Adversarial Networks","date":"2018-10-11","arxiv_id":"1810.05221","n_code_links":0,"syntology":null},{"paper":null,"slug":"taming-the-cross-entropy-loss","title":"Taming the Cross Entropy Loss","date":"2018-10-11","arxiv_id":"1810.05075","n_code_links":0,"syntology":null},{"paper":"/paper/an-evaluation-metric-for-object-detection","slug":"an-evaluation-metric-for-object-detection","title":"An evaluation metric for object detection algorithms in autonomous navigation systems and its application to a real-time alerting system","date":"2018-10-10","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"image-super-resolution-using-vdsr-resnext-and","title":"Image Super-Resolution Using VDSR-ResNeXt and SRCGAN","date":"2018-10-10","arxiv_id":"1810.05731","n_code_links":0,"syntology":null},{"paper":"/paper/lirs-enabling-efficient-machine-learning-on","slug":"lirs-enabling-efficient-machine-learning-on","title":"LIRS: Enabling efficient machine learning on NVM-based storage via a lightweight implementation of random shuffling","date":"2018-10-10","arxiv_id":"1810.04509","n_code_links":1,"syntology":null},{"paper":null,"slug":"non-linear-process-convolutions-for-multi","title":"Non-linear process convolutions for multi-output Gaussian processes","date":"2018-10-10","arxiv_id":"1810.04632","n_code_links":0,"syntology":null},{"paper":"/paper/parametrized-deep-q-networks-learning","slug":"parametrized-deep-q-networks-learning","title":"Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space","date":"2018-10-10","arxiv_id":"1810.06394","n_code_links":5,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/training-generative-adversarial-networks-with-1","slug":"training-generative-adversarial-networks-with-1","title":"Training Generative Adversarial Networks with Binary Neurons by End-to-end Backpropagation","date":"2018-10-10","arxiv_id":"1810.04714","n_code_links":1,"syntology":null},{"paper":null,"slug":"unpaired-high-resolution-and-scalable-style","title":"Unpaired High-Resolution and Scalable Style Transfer Using Generative Adversarial Networks","date":"2018-10-10","arxiv_id":"1810.05724","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparison-of-u-net-based-convolutional","title":"Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT","date":"2018-10-09","arxiv_id":"1810.04017","n_code_links":0,"syntology":null},{"paper":"/paper/deepweeds-a-multiclass-weed-species-image","slug":"deepweeds-a-multiclass-weed-species-image","title":"DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning","date":"2018-10-09","arxiv_id":"1810.05726","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["AlexOlsen/DeepWeeds"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"generalized-latent-variable-recovery-for","title":"Generalized Latent Variable Recovery for Generative Adversarial Networks","date":"2018-10-09","arxiv_id":"1810.03764","n_code_links":0,"syntology":null}],"record_sha256":"8c00ce20bed8e957ad93a6c2a5deff3d8b7125714675c5036d6e5b06168e5a33","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}