{"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/194","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":194,"pages_in_order":196,"rows_per_page":100,"rows":[19301,19400],"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/193","next":"/method/convolution/papers/195","papers":[{"paper":"/paper/image-restoration-using-convolutional-auto","slug":"image-restoration-using-convolutional-auto","title":"Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections","date":"2016-06-29","arxiv_id":"1606.08921","n_code_links":17,"syntology":null},{"paper":"/paper/3d-u-net-learning-dense-volumetric","slug":"3d-u-net-learning-dense-volumetric","title":"3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation","date":"2016-06-21","arxiv_id":"1606.06650","n_code_links":27,"syntology":{"ran":59,"of":76,"n_ran_checked":42,"n_instrument":17,"unverified":17,"pointer_only":20,"phrase":"59 ran (of which 18 constructed an object rather than computing a result; 42 with no instrument failure: 4 honoured, 0 violated, 38 with no contract checked; 17 where Syntology's instrument failed) · 17 unverified","official":null}},{"paper":"/paper/dorefa-net-training-low-bitwidth","slug":"dorefa-net-training-low-bitwidth","title":"DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients","date":"2016-06-20","arxiv_id":"1606.06160","n_code_links":13,"syntology":{"ran":8,"of":13,"n_ran_checked":8,"n_instrument":0,"unverified":5,"pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["tensorpack/tensorpack"],"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/learning-convolutional-neural-networks-using","slug":"learning-convolutional-neural-networks-using","title":"Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and Estimation","date":"2016-06-20","arxiv_id":"1606.05929","n_code_links":1,"syntology":null},{"paper":"/paper/cms-rcnn-contextual-multi-scale-region-based","slug":"cms-rcnn-contextual-multi-scale-region-based","title":"CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection","date":"2016-06-17","arxiv_id":"1606.05413","n_code_links":0,"syntology":null},{"paper":null,"slug":"znni-maximizing-the-inference-throughput-of","title":"ZNNi - Maximizing the Inference Throughput of 3D Convolutional Networks on Multi-Core CPUs and GPUs","date":"2016-06-17","arxiv_id":"1606.05688","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-reinforcement-learning-discovers","title":"Deep Reinforcement Learning Discovers Internal Models","date":"2016-06-16","arxiv_id":"1606.05174","n_code_links":0,"syntology":null},{"paper":null,"slug":"cltorch-a-hardware-agnostic-backend-for-the","title":"cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL","date":"2016-06-15","arxiv_id":"1606.04884","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-reinforcement-learning-with-macro","title":"Deep Reinforcement Learning With Macro-Actions","date":"2016-06-15","arxiv_id":"1606.04615","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-modal-hybrid-deep-neural-network-for","title":"Multi-Modal Hybrid Deep Neural Network for Speech Enhancement","date":"2016-06-15","arxiv_id":"1606.04750","n_code_links":0,"syntology":null},{"paper":null,"slug":"richardson-lucy-deblurring-for-moving-light","title":"Richardson-Lucy Deblurring for Moving Light Field Cameras","date":"2016-06-14","arxiv_id":"1606.04308","n_code_links":0,"syntology":null},{"paper":"/paper/infogan-interpretable-representation-learning","slug":"infogan-interpretable-representation-learning","title":"InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets","date":"2016-06-12","arxiv_id":"1606.03657","n_code_links":38,"syntology":{"ran":5,"of":6,"n_ran_checked":1,"n_instrument":4,"unverified":1,"pointer_only":0,"phrase":"5 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; 4 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":"/paper/training-recurrent-answering-units-with-joint","slug":"training-recurrent-answering-units-with-joint","title":"Training Recurrent Answering Units with Joint Loss Minimization for VQA","date":"2016-06-12","arxiv_id":"1606.03647","n_code_links":0,"syntology":null},{"paper":"/paper/face-detection-with-the-faster-r-cnn","slug":"face-detection-with-the-faster-r-cnn","title":"Face Detection with the Faster R-CNN","date":"2016-06-10","arxiv_id":"1606.03473","n_code_links":1,"syntology":null},{"paper":"/paper/improved-techniques-for-training-gans","slug":"improved-techniques-for-training-gans","title":"Improved Techniques for Training GANs","date":"2016-06-10","arxiv_id":"1606.03498","n_code_links":46,"syntology":{"ran":1,"of":2,"n_ran_checked":0,"n_instrument":1,"unverified":1,"pointer_only":0,"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":["openai/improved-gan","openai/improved_gan"],"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":"fully-convolutional-networks-for-dense","title":"Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery","date":"2016-06-08","arxiv_id":"1606.02585","n_code_links":0,"syntology":null},{"paper":"/paper/enet-a-deep-neural-network-architecture-for","slug":"enet-a-deep-neural-network-architecture-for","title":"ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation","date":"2016-06-07","arxiv_id":"1606.02147","n_code_links":49,"syntology":{"ran":26,"of":30,"n_ran_checked":22,"n_instrument":4,"unverified":4,"pointer_only":2,"phrase":"26 ran (of which 0 constructed an object rather than computing a result; 22 with no instrument failure: 0 honoured, 0 violated, 22 with no contract checked; 4 where Syntology's instrument failed) · 4 unverified","official":null}},{"paper":"/paper/systematic-evaluation-of-cnn-advances-on-the","slug":"systematic-evaluation-of-cnn-advances-on-the","title":"Systematic evaluation of CNN advances on the ImageNet","date":"2016-06-07","arxiv_id":"1606.02228","n_code_links":1,"syntology":null},{"paper":"/paper/multimodal-compact-bilinear-pooling-for","slug":"multimodal-compact-bilinear-pooling-for","title":"Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding","date":"2016-06-06","arxiv_id":"1606.01847","n_code_links":10,"syntology":null},{"paper":"/paper/semi-supervised-learning-with-generative","slug":"semi-supervised-learning-with-generative","title":"Semi-Supervised Learning with Generative Adversarial Networks","date":"2016-06-05","arxiv_id":"1606.01583","n_code_links":7,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"shallow-networks-for-high-accuracy-road","title":"Shallow Networks for High-Accuracy Road Object-Detection","date":"2016-06-05","arxiv_id":"1606.01561","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-tale-of-two-bases-local-nonlocal","title":"A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets","date":"2016-06-04","arxiv_id":"1606.01377","n_code_links":0,"syntology":null},{"paper":"/paper/on-valid-optimal-assignment-kernels-and","slug":"on-valid-optimal-assignment-kernels-and","title":"On Valid Optimal Assignment Kernels and Applications to Graph Classification","date":"2016-06-03","arxiv_id":"1606.01141","n_code_links":0,"syntology":null},{"paper":"/paper/adversarially-learned-inference","slug":"adversarially-learned-inference","title":"Adversarially Learned Inference","date":"2016-06-02","arxiv_id":"1606.00704","n_code_links":9,"syntology":{"ran":3,"of":4,"n_ran_checked":2,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["IshmaelBelghazi/ALI"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/deeplab-semantic-image-segmentation-with-deep","slug":"deeplab-semantic-image-segmentation-with-deep","title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","date":"2016-06-02","arxiv_id":"1606.00915","n_code_links":47,"syntology":{"ran":43,"of":63,"n_ran_checked":39,"n_instrument":4,"unverified":20,"pointer_only":18,"phrase":"43 ran (of which 12 constructed an object rather than computing a result; 39 with no instrument failure: 1 honoured, 0 violated, 38 with no contract checked; 4 where Syntology's instrument failed) · 20 unverified","official":null}},{"paper":null,"slug":"a-comparative-study-of-algorithms-for","title":"A Comparative Study of Algorithms for Realtime Panoramic Video Blending","date":"2016-06-01","arxiv_id":"1606.00103","n_code_links":0,"syntology":null},{"paper":"/paper/composition-preserving-deep-photo-aesthetics","slug":"composition-preserving-deep-photo-aesthetics","title":"Composition-Preserving Deep Photo Aesthetics Assessment","date":"2016-06-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"recurrent-fully-convolutional-networks-for","title":"Recurrent Fully Convolutional Networks for Video Segmentation","date":"2016-06-01","arxiv_id":"1606.00487","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-feature-learning","slug":"adversarial-feature-learning","title":"Adversarial Feature Learning","date":"2016-05-31","arxiv_id":"1605.09782","n_code_links":10,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 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; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/hierarchical-question-image-co-attention-for","slug":"hierarchical-question-image-co-attention-for","title":"Hierarchical Question-Image Co-Attention for Visual Question Answering","date":"2016-05-31","arxiv_id":"1606.00061","n_code_links":9,"syntology":{"ran":4,"of":7,"n_ran_checked":3,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"4 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; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["jiasenlu/HieCoAttenVQA"],"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":"the-use-of-deep-learning-in-image","title":"The use of deep learning in image segmentation, classification and detection","date":"2016-05-31","arxiv_id":"1605.09612","n_code_links":0,"syntology":null},{"paper":null,"slug":"going-deeper-for-multilingual-visual","title":"Going Deeper for Multilingual Visual Sentiment Detection","date":"2016-05-30","arxiv_id":"1605.09211","n_code_links":0,"syntology":null},{"paper":"/paper/parametric-exponential-linear-unit-for-deep","slug":"parametric-exponential-linear-unit-for-deep","title":"Parametric Exponential Linear Unit for Deep Convolutional Neural Networks","date":"2016-05-30","arxiv_id":"1605.09332","n_code_links":0,"syntology":null},{"paper":"/paper/density-estimation-using-real-nvp","slug":"density-estimation-using-real-nvp","title":"Density estimation using Real NVP","date":"2016-05-27","arxiv_id":"1605.08803","n_code_links":35,"syntology":{"ran":48,"of":70,"n_ran_checked":38,"n_instrument":10,"unverified":22,"pointer_only":33,"phrase":"48 ran (of which 26 constructed an object rather than computing a result; 38 with no instrument failure: 2 honoured, 0 violated, 36 with no contract checked; 10 where Syntology's instrument failed) · 22 unverified","official":null}},{"paper":"/paper/theano-mpi-a-theano-based-distributed","slug":"theano-mpi-a-theano-based-distributed","title":"Theano-MPI: a Theano-based Distributed Training Framework","date":"2016-05-26","arxiv_id":"1605.08325","n_code_links":1,"syntology":null},{"paper":null,"slug":"convolutional-random-walk-networks-for","title":"Convolutional Random Walk Networks for Semantic Image Segmentation","date":"2016-05-24","arxiv_id":"1605.07681","n_code_links":0,"syntology":null},{"paper":null,"slug":"dense-cnn-learning-with-equivalent-mappings","title":"Dense CNN Learning with Equivalent Mappings","date":"2016-05-24","arxiv_id":"1605.07251","n_code_links":0,"syntology":null},{"paper":null,"slug":"eventnet-version-11-technical-report","title":"EventNet Version 1.1 Technical Report","date":"2016-05-24","arxiv_id":"1605.07289","n_code_links":0,"syntology":null},{"paper":"/paper/fractalnet-ultra-deep-neural-networks-without","slug":"fractalnet-ultra-deep-neural-networks-without","title":"FractalNet: Ultra-Deep Neural Networks without Residuals","date":"2016-05-24","arxiv_id":"1605.07648","n_code_links":4,"syntology":{"ran":4,"of":6,"n_ran_checked":0,"n_instrument":4,"unverified":2,"pointer_only":1,"phrase":"4 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; 4 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/self-paced-deep-learning-for-weakly","slug":"self-paced-deep-learning-for-weakly","title":"Self Paced Deep Learning for Weakly Supervised Object Detection","date":"2016-05-24","arxiv_id":"1605.07651","n_code_links":1,"syntology":null},{"paper":"/paper/wide-residual-networks","slug":"wide-residual-networks","title":"Wide Residual Networks","date":"2016-05-23","arxiv_id":"1605.07146","n_code_links":72,"syntology":{"ran":66,"of":96,"n_ran_checked":48,"n_instrument":18,"unverified":30,"pointer_only":50,"phrase":"66 ran (of which 31 constructed an object rather than computing a result; 48 with no instrument failure: 0 honoured, 0 violated, 48 with no contract checked; 18 where Syntology's instrument failed) · 30 unverified","official":{"repos":["szagoruyko/wide-residual-networks"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":"/paper/inductive-bias-of-deep-convolutional-networks","slug":"inductive-bias-of-deep-convolutional-networks","title":"Inductive Bias of Deep Convolutional Networks through Pooling Geometry","date":"2016-05-22","arxiv_id":"1605.06743","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-roots-improving-cnn-efficiency-with","title":"Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups","date":"2016-05-20","arxiv_id":"1605.06489","n_code_links":0,"syntology":null},{"paper":"/paper/fpnn-field-probing-neural-networks-for-3d","slug":"fpnn-field-probing-neural-networks-for-3d","title":"FPNN: Field Probing Neural Networks for 3D Data","date":"2016-05-20","arxiv_id":"1605.06240","n_code_links":2,"syntology":null},{"paper":"/paper/fully-convolutional-networks-for-semantic","slug":"fully-convolutional-networks-for-semantic","title":"Fully Convolutional Networks for Semantic Segmentation","date":"2016-05-20","arxiv_id":"1605.06211","n_code_links":37,"syntology":null},{"paper":"/paper/r-fcn-object-detection-via-region-based-fully","slug":"r-fcn-object-detection-via-region-based-fully","title":"R-FCN: Object Detection via Region-based Fully Convolutional Networks","date":"2016-05-20","arxiv_id":"1605.06409","n_code_links":48,"syntology":{"ran":7,"of":11,"n_ran_checked":6,"n_instrument":1,"unverified":4,"pointer_only":1,"phrase":"7 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; 1 where Syntology's instrument failed) · 4 unverified","official":{"repos":["daijifeng001/r-fcn"],"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/ristretto-hardware-oriented-approximation-of","slug":"ristretto-hardware-oriented-approximation-of","title":"Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks","date":"2016-05-20","arxiv_id":"1605.06402","n_code_links":2,"syntology":null},{"paper":null,"slug":"swapout-learning-an-ensemble-of-deep","title":"Swapout: Learning an ensemble of deep architectures","date":"2016-05-20","arxiv_id":"1605.06465","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-frame-skip-deep-q-network","title":"Dynamic Frame skip Deep Q Network","date":"2016-05-17","arxiv_id":"1605.05365","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-and-accurate-algorithm-for-eye","title":"Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images","date":"2016-05-17","arxiv_id":"1605.05272","n_code_links":0,"syntology":null},{"paper":"/paper/deepercut-a-deeper-stronger-and-faster-multi","slug":"deepercut-a-deeper-stronger-and-faster-multi","title":"DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model","date":"2016-05-10","arxiv_id":"1605.03170","n_code_links":16,"syntology":null},{"paper":"/paper/must-cnn-a-multilayer-shift-and-stitch-deep","slug":"must-cnn-a-multilayer-shift-and-stitch-deep","title":"MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction","date":"2016-05-10","arxiv_id":"1605.03004","n_code_links":3,"syntology":null},{"paper":null,"slug":"when-do-luxury-cars-hit-the-road-findings-by","title":"When Do Luxury Cars Hit the Road? Findings by A Big Data Approach","date":"2016-05-10","arxiv_id":"1605.02827","n_code_links":0,"syntology":null},{"paper":"/paper/visual-saliency-based-on-scale-space-analysis","slug":"visual-saliency-based-on-scale-space-analysis","title":"Visual Saliency Based on Scale-Space Analysis in the Frequency Domain","date":"2016-05-06","arxiv_id":"1605.01999","n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-diversity-and-hard-positive","title":"Adversarial Diversity and Hard Positive Generation","date":"2016-05-05","arxiv_id":"1605.01775","n_code_links":0,"syntology":null},{"paper":null,"slug":"accelerating-deep-learning-with-shrinkage-and","title":"Accelerating Deep Learning with Shrinkage and Recall","date":"2016-05-04","arxiv_id":"1605.01369","n_code_links":0,"syntology":null},{"paper":null,"slug":"fourier-analysis-and-q-gaussian-functions","title":"Fourier Analysis and q-Gaussian Functions: Analytical and Numerical Results","date":"2016-05-02","arxiv_id":"1605.00452","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-convolutional-neural-networks-on-cartoon","title":"Deep Convolutional Neural Networks on Cartoon Functions","date":"2016-04-29","arxiv_id":"1605.00031","n_code_links":0,"syntology":null},{"paper":"/paper/faster-r-cnn-features-for-instance-search","slug":"faster-r-cnn-features-for-instance-search","title":"Faster R-CNN Features for Instance Search","date":"2016-04-29","arxiv_id":"1604.08893","n_code_links":3,"syntology":null},{"paper":null,"slug":"classifying-options-for-deep-reinforcement","title":"Classifying Options for Deep Reinforcement Learning","date":"2016-04-27","arxiv_id":"1604.08153","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-fold-gabor-pca-and-ica-filter","title":"Multi-Fold Gabor, PCA and ICA Filter Convolution Descriptor for Face Recognition","date":"2016-04-24","arxiv_id":"1604.07057","n_code_links":0,"syntology":null},{"paper":"/paper/convolutional-two-stream-network-fusion-for","slug":"convolutional-two-stream-network-fusion-for","title":"Convolutional Two-Stream Network Fusion for Video Action Recognition","date":"2016-04-22","arxiv_id":"1604.06573","n_code_links":2,"syntology":null},{"paper":null,"slug":"refining-architectures-of-deep-convolutional","title":"Refining Architectures of Deep Convolutional Neural Networks","date":"2016-04-22","arxiv_id":"1604.06832","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-feature-based-contextual-model-for","title":"Deep Feature Based Contextual Model for Object Detection","date":"2016-04-14","arxiv_id":"1604.04048","n_code_links":0,"syntology":null},{"paper":"/paper/deep-residual-networks-with-exponential","slug":"deep-residual-networks-with-exponential","title":"Deep Residual Networks with Exponential Linear Unit","date":"2016-04-14","arxiv_id":"1604.04112","n_code_links":1,"syntology":null},{"paper":"/paper/object-detection-from-video-tubelets-with","slug":"object-detection-from-video-tubelets-with","title":"Object Detection from Video Tubelets with Convolutional Neural Networks","date":"2016-04-14","arxiv_id":"1604.04053","n_code_links":1,"syntology":null},{"paper":"/paper/bridging-the-gaps-between-residual-learning","slug":"bridging-the-gaps-between-residual-learning","title":"Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex","date":"2016-04-13","arxiv_id":"1604.03640","n_code_links":4,"syntology":null},{"paper":"/paper/craft-objects-from-images","slug":"craft-objects-from-images","title":"CRAFT Objects from Images","date":"2016-04-12","arxiv_id":"1604.03239","n_code_links":1,"syntology":null},{"paper":"/paper/hardware-oriented-approximation-of","slug":"hardware-oriented-approximation-of","title":"Hardware-oriented Approximation of Convolutional Neural Networks","date":"2016-04-11","arxiv_id":"1604.03168","n_code_links":1,"syntology":null},{"paper":"/paper/t-cnn-tubelets-with-convolutional-neural","slug":"t-cnn-tubelets-with-convolutional-neural","title":"T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos","date":"2016-04-09","arxiv_id":"1604.02532","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"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) · 0 unverified","official":{"repos":["myfavouritekk/T-CNN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/a-multipath-network-for-object-detection","slug":"a-multipath-network-for-object-detection","title":"A MultiPath Network for Object Detection","date":"2016-04-07","arxiv_id":"1604.02135","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-bias-non-linear-activation-in-deep","title":"Multi-Bias Non-linear Activation in Deep Neural Networks","date":"2016-04-03","arxiv_id":"1604.00676","n_code_links":0,"syntology":null},{"paper":"/paper/image-restoration-using-very-deep","slug":"image-restoration-using-very-deep","title":"Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections","date":"2016-03-30","arxiv_id":"1603.09056","n_code_links":3,"syntology":null},{"paper":null,"slug":"structured-feature-learning-for-pose","title":"Structured Feature Learning for Pose Estimation","date":"2016-03-30","arxiv_id":"1603.09065","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-learning-of-visual-1","slug":"unsupervised-learning-of-visual-1","title":"Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles","date":"2016-03-30","arxiv_id":"1603.09246","n_code_links":9,"syntology":{"ran":10,"of":13,"n_ran_checked":7,"n_instrument":3,"unverified":3,"pointer_only":7,"phrase":"10 ran (of which 6 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":null,"slug":"instance-sensitive-fully-convolutional","title":"Instance-sensitive Fully Convolutional Networks","date":"2016-03-29","arxiv_id":"1603.08678","n_code_links":0,"syntology":null},{"paper":"/paper/learning-to-refine-object-segments","slug":"learning-to-refine-object-segments","title":"Learning to Refine Object Segments","date":"2016-03-29","arxiv_id":"1603.08695","n_code_links":2,"syntology":null},{"paper":null,"slug":"convolutional-networks-for-fast-energy","title":"Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing","date":"2016-03-28","arxiv_id":"1603.08270","n_code_links":0,"syntology":null},{"paper":"/paper/resnet-in-resnet-generalizing-residual","slug":"resnet-in-resnet-generalizing-residual","title":"Resnet in Resnet: Generalizing Residual Architectures","date":"2016-03-25","arxiv_id":"1603.08029","n_code_links":1,"syntology":null},{"paper":null,"slug":"attentive-contexts-for-object-detection","title":"Attentive Contexts for Object Detection","date":"2016-03-24","arxiv_id":"1603.07415","n_code_links":0,"syntology":null},{"paper":null,"slug":"convolution-in-convolution-for-network-in","title":"Convolution in Convolution for Network in Network","date":"2016-03-22","arxiv_id":"1603.06759","n_code_links":0,"syntology":null},{"paper":"/paper/stacked-hourglass-networks-for-human-pose","slug":"stacked-hourglass-networks-for-human-pose","title":"Stacked Hourglass Networks for Human Pose Estimation","date":"2016-03-22","arxiv_id":"1603.06937","n_code_links":46,"syntology":{"ran":24,"of":25,"n_ran_checked":22,"n_instrument":2,"unverified":1,"pointer_only":1,"phrase":"24 ran (of which 0 constructed an object rather than computing a result; 22 with no instrument failure: 0 honoured, 0 violated, 22 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"deep-self-convolutional-activations","title":"Deep Self-Convolutional Activations Descriptor for Dense Cross-Modal Correspondence","date":"2016-03-21","arxiv_id":"1603.06327","n_code_links":0,"syntology":null},{"paper":null,"slug":"fractal-dimension-invariant-filtering-and-its","title":"Fractal Dimension Invariant Filtering and Its CNN-based Implementation","date":"2016-03-19","arxiv_id":"1603.06036","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparing-time-and-frequency-domain-for-audio","title":"Comparing Time and Frequency Domain for Audio Event Recognition Using Deep Learning","date":"2016-03-18","arxiv_id":"1603.05824","n_code_links":0,"syntology":null},{"paper":null,"slug":"accelerating-deep-neural-network-training","title":"Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent","date":"2016-03-17","arxiv_id":"1603.05544","n_code_links":0,"syntology":null},{"paper":"/paper/identity-mappings-in-deep-residual-networks","slug":"identity-mappings-in-deep-residual-networks","title":"Identity Mappings in Deep Residual Networks","date":"2016-03-16","arxiv_id":"1603.05027","n_code_links":54,"syntology":{"ran":14,"of":25,"n_ran_checked":12,"n_instrument":2,"unverified":11,"pointer_only":2,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 11 unverified","official":{"repos":["KaimingHe/resnet-1k-layers"],"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/understanding-and-improving-convolutional","slug":"understanding-and-improving-convolutional","title":"Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units","date":"2016-03-16","arxiv_id":"1603.05201","n_code_links":2,"syntology":null},{"paper":"/paper/xnor-net-imagenet-classification-using-binary","slug":"xnor-net-imagenet-classification-using-binary","title":"XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks","date":"2016-03-16","arxiv_id":"1603.05279","n_code_links":20,"syntology":{"ran":16,"of":18,"n_ran_checked":9,"n_instrument":7,"unverified":2,"pointer_only":3,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 7 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"multichannel-variable-size-convolution-for","title":"Multichannel Variable-Size Convolution for Sentence Classification","date":"2016-03-15","arxiv_id":"1603.04513","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-forward-propagation-of-time","title":"Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting","date":"2016-03-11","arxiv_id":"1603.03657","n_code_links":0,"syntology":null},{"paper":"/paper/hyperface-a-deep-multi-task-learning","slug":"hyperface-a-deep-multi-task-learning","title":"HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition","date":"2016-03-03","arxiv_id":"1603.01249","n_code_links":2,"syntology":null},{"paper":null,"slug":"mgnc-cnn-a-simple-approach-to-exploiting","title":"MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification","date":"2016-03-03","arxiv_id":"1603.00968","n_code_links":0,"syntology":null},{"paper":"/paper/vdnn-virtualized-deep-neural-networks-for","slug":"vdnn-virtualized-deep-neural-networks-for","title":"vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design","date":"2016-02-25","arxiv_id":"1602.08124","n_code_links":4,"syntology":null},{"paper":"/paper/group-equivariant-convolutional-networks","slug":"group-equivariant-convolutional-networks","title":"Group Equivariant Convolutional Networks","date":"2016-02-24","arxiv_id":"1602.07576","n_code_links":1,"syntology":null},{"paper":"/paper/squeezenet-alexnet-level-accuracy-with-50x","slug":"squeezenet-alexnet-level-accuracy-with-50x","title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","date":"2016-02-24","arxiv_id":"1602.07360","n_code_links":59,"syntology":{"ran":4,"of":4,"n_ran_checked":0,"n_instrument":4,"unverified":0,"pointer_only":2,"phrase":"4 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; 4 where Syntology's instrument failed) · 0 unverified","official":{"repos":["DT42/squeezenet_demo","DeepScale/SqueezeNet","Element-Research/dpnn","ejlb/squeezenet-chainer","haria/SqueezeNet"],"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/inception-v4-inception-resnet-and-the-impact","slug":"inception-v4-inception-resnet-and-the-impact","title":"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning","date":"2016-02-23","arxiv_id":"1602.07261","n_code_links":87,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"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) · 0 unverified","official":null}},{"paper":"/paper/randomout-using-a-convolutional-gradient-norm","slug":"randomout-using-a-convolutional-gradient-norm","title":"RandomOut: Using a convolutional gradient norm to rescue convolutional filters","date":"2016-02-18","arxiv_id":"1602.05931","n_code_links":1,"syntology":null},{"paper":null,"slug":"revise-saturated-activation-functions","title":"Revise Saturated Activation Functions","date":"2016-02-18","arxiv_id":"1602.05980","n_code_links":0,"syntology":null},{"paper":null,"slug":"deconvolutional-feature-stacking-for-weakly","title":"Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation","date":"2016-02-16","arxiv_id":"1602.04984","n_code_links":0,"syntology":null}],"record_sha256":"29278aa17b54b8066c19f651b6fbb91cdcce260de8155955e8943ef90ec655f8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}