{"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/180","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":180,"pages_in_order":196,"rows_per_page":100,"rows":[17901,18000],"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/179","next":"/method/convolution/papers/181","papers":[{"paper":"/paper/sniper-efficient-multi-scale-training","slug":"sniper-efficient-multi-scale-training","title":"SNIPER: Efficient Multi-Scale Training","date":"2018-05-23","arxiv_id":"1805.09300","n_code_links":4,"syntology":null},{"paper":null,"slug":"a-solvable-high-dimensional-model-of-gan","title":"A Solvable High-Dimensional Model of GAN","date":"2018-05-22","arxiv_id":"1805.08349","n_code_links":0,"syntology":null},{"paper":null,"slug":"cascadecnn-pushing-the-performance-limits-of","title":"CascadeCNN: Pushing the performance limits of quantisation","date":"2018-05-22","arxiv_id":"1805.08743","n_code_links":0,"syntology":null},{"paper":null,"slug":"improved-person-detection-on-omnidirectional","title":"Improved Person Detection on Omnidirectional Images with Non-maxima Suppression","date":"2018-05-22","arxiv_id":"1805.08503","n_code_links":0,"syntology":null},{"paper":null,"slug":"knowledge-based-fully-convolutional-network","title":"Knowledge-based Fully Convolutional Network and Its Application in Segmentation of Lung CT Images","date":"2018-05-22","arxiv_id":"1805.08492","n_code_links":0,"syntology":null},{"paper":"/paper/meta-learning-with-hessian-free-approach-in","slug":"meta-learning-with-hessian-free-approach-in","title":"Meta-Learning with Hessian-Free Approach in Deep Neural Nets Training","date":"2018-05-22","arxiv_id":"1805.08462","n_code_links":1,"syntology":null},{"paper":"/paper/robust-conditional-generative-adversarial","slug":"robust-conditional-generative-adversarial","title":"Robust Conditional Generative Adversarial Networks","date":"2018-05-22","arxiv_id":"1805.08657","n_code_links":1,"syntology":null},{"paper":null,"slug":"training-convolutional-networks-with-web","title":"Training Convolutional Networks with Web Images","date":"2018-05-22","arxiv_id":"1805.08416","n_code_links":0,"syntology":null},{"paper":null,"slug":"compression-of-deep-convolutional-neural-1","title":"Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints","date":"2018-05-21","arxiv_id":"1805.08303","n_code_links":0,"syntology":null},{"paper":null,"slug":"parallel-transport-convolution-a-new-tool-for","title":"Parallel Transport Convolution: A New Tool for Convolutional Neural Networks on Manifolds","date":"2018-05-21","arxiv_id":"1805.07857","n_code_links":0,"syntology":null},{"paper":"/paper/self-attention-generative-adversarial","slug":"self-attention-generative-adversarial","title":"Self-Attention Generative Adversarial Networks","date":"2018-05-21","arxiv_id":"1805.08318","n_code_links":48,"syntology":{"ran":46,"of":81,"n_ran_checked":42,"n_instrument":4,"unverified":35,"pointer_only":3,"phrase":"46 ran (of which 0 constructed an object rather than computing a result; 42 with no instrument failure: 2 honoured, 0 violated, 40 with no contract checked; 4 where Syntology's instrument failed) · 35 unverified","official":{"repos":["brain-research/self-attention-gan"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":7,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":"/paper/small-steps-and-giant-leaps-minimal-newton","slug":"small-steps-and-giant-leaps-minimal-newton","title":"Small steps and giant leaps: Minimal Newton solvers for Deep Learning","date":"2018-05-21","arxiv_id":"1805.08095","n_code_links":6,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 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; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["jotaf98/curveball"],"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":"spherical-convolutional-neural-network-for-3d","title":"Spherical Convolutional Neural Network for 3D Point Clouds","date":"2018-05-21","arxiv_id":"1805.07872","n_code_links":0,"syntology":null},{"paper":"/paper/abstractive-text-classification-using","slug":"abstractive-text-classification-using","title":"Abstractive Text Classification Using Sequence-to-convolution Neural Networks","date":"2018-05-20","arxiv_id":"1805.07745","n_code_links":1,"syntology":null},{"paper":null,"slug":"object-localization-and-motion-transfer","title":"Object Localization with a Weakly Supervised CapsNet","date":"2018-05-20","arxiv_id":"1805.07706","n_code_links":0,"syntology":null},{"paper":"/paper/bourgan-generative-networks-with-metric","slug":"bourgan-generative-networks-with-metric","title":"BourGAN: Generative Networks with Metric Embeddings","date":"2018-05-19","arxiv_id":"1805.07674","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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) · 0 unverified","official":null}},{"paper":null,"slug":"cappronet-deep-feature-learning-via","title":"CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces","date":"2018-05-19","arxiv_id":"1805.07621","n_code_links":0,"syntology":null},{"paper":null,"slug":"episodic-memory-deep-q-networks","title":"Episodic Memory Deep Q-Networks","date":"2018-05-19","arxiv_id":"1805.07603","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-structure-matching-loss-for-image","slug":"adversarial-structure-matching-loss-for-image","title":"Adversarial Structure Matching for Structured Prediction Tasks","date":"2018-05-18","arxiv_id":"1805.07457","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-unsupervised-approach-to-solving-inverse","title":"An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks","date":"2018-05-18","arxiv_id":"1805.07281","n_code_links":0,"syntology":null},{"paper":"/paper/fast-kernel-approximations-for-latent-force","slug":"fast-kernel-approximations-for-latent-force","title":"Fast Kernel Approximations for Latent Force Models and Convolved Multiple-Output Gaussian processes","date":"2018-05-18","arxiv_id":"1805.07460","n_code_links":1,"syntology":null},{"paper":"/paper/multi-level-wavelet-cnn-for-image-restoration","slug":"multi-level-wavelet-cnn-for-image-restoration","title":"Multi-level Wavelet-CNN for Image Restoration","date":"2018-05-18","arxiv_id":"1805.07071","n_code_links":5,"syntology":null},{"paper":"/paper/norm-preservation-why-residual-networks-can","slug":"norm-preservation-why-residual-networks-can","title":"Norm-Preservation: Why Residual Networks Can Become Extremely Deep?","date":"2018-05-18","arxiv_id":"1805.07477","n_code_links":1,"syntology":null},{"paper":"/paper/the-eurocity-persons-dataset-a-novel","slug":"the-eurocity-persons-dataset-a-novel","title":"The EuroCity Persons Dataset: A Novel Benchmark for Object Detection","date":"2018-05-18","arxiv_id":"1805.07193","n_code_links":0,"syntology":null},{"paper":"/paper/understanding-and-improving-deep-neural","slug":"understanding-and-improving-deep-neural","title":"Understanding and Improving Deep Neural Network for Activity Recognition","date":"2018-05-18","arxiv_id":"1805.07020","n_code_links":2,"syntology":null},{"paper":null,"slug":"identifying-object-states-in-cooking-related","title":"Identifying Object States in Cooking-Related Images","date":"2018-05-17","arxiv_id":"1805.06956","n_code_links":0,"syntology":null},{"paper":null,"slug":"interpolatron-interpolation-or-extrapolation","title":"Interpolatron: Interpolation or Extrapolation Schemes to Accelerate Optimization for Deep Neural Networks","date":"2018-05-17","arxiv_id":"1805.06753","n_code_links":0,"syntology":null},{"paper":null,"slug":"approximating-the-void-learning-stochastic","title":"Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks","date":"2018-05-16","arxiv_id":"1805.06350","n_code_links":0,"syntology":null},{"paper":"/paper/deep-segmentation-and-registration-in-x-ray","slug":"deep-segmentation-and-registration-in-x-ray","title":"Deep Segmentation and Registration in X-Ray Angiography Video","date":"2018-05-16","arxiv_id":"1805.06406","n_code_links":1,"syntology":null},{"paper":null,"slug":"graph-edge-convolutional-neural-networks-for","title":"Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition","date":"2018-05-16","arxiv_id":"1805.06184","n_code_links":0,"syntology":null},{"paper":"/paper/object-detection-at-200-frames-per-second","slug":"object-detection-at-200-frames-per-second","title":"Object detection at 200 Frames Per Second","date":"2018-05-16","arxiv_id":"1805.06361","n_code_links":1,"syntology":null},{"paper":null,"slug":"optimized-computation-offloading-performance","title":"Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning","date":"2018-05-16","arxiv_id":"1805.06146","n_code_links":0,"syntology":null},{"paper":"/paper/advances-in-experience-replay","slug":"advances-in-experience-replay","title":"Advances in Experience Replay","date":"2018-05-15","arxiv_id":"1805.05536","n_code_links":1,"syntology":null},{"paper":"/paper/do-deep-reinforcement-learning-agents-model","slug":"do-deep-reinforcement-learning-agents-model","title":"Do deep reinforcement learning agents model intentions?","date":"2018-05-15","arxiv_id":"1805.06020","n_code_links":1,"syntology":null},{"paper":"/paper/cycle-dehaze-enhanced-cyclegan-for-single","slug":"cycle-dehaze-enhanced-cyclegan-for-single","title":"Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing","date":"2018-05-14","arxiv_id":"1805.05308","n_code_links":3,"syntology":null},{"paper":null,"slug":"generative-adversarial-forests-for-better","title":"Generative Adversarial Forests for Better Conditioned Adversarial Learning","date":"2018-05-14","arxiv_id":"1805.05185","n_code_links":0,"syntology":null},{"paper":"/paper/gan-q-learning","slug":"gan-q-learning","title":"GAN Q-learning","date":"2018-05-13","arxiv_id":"1805.04874","n_code_links":1,"syntology":null},{"paper":"/paper/learning-rich-features-for-image-manipulation","slug":"learning-rich-features-for-image-manipulation","title":"Learning Rich Features for Image Manipulation Detection","date":"2018-05-13","arxiv_id":"1805.04953","n_code_links":2,"syntology":{"ran":6,"of":8,"n_ran_checked":6,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 3 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":null,"slug":"classification-of-protein-crystallization-x","title":"Classification of Protein Crystallization X-Ray Images Using Major Convolutional Neural Network Architectures","date":"2018-05-11","arxiv_id":"1805.04563","n_code_links":0,"syntology":null},{"paper":null,"slug":"exploiting-images-for-video-recognition-with","title":"Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks","date":"2018-05-11","arxiv_id":"1805.04384","n_code_links":0,"syntology":null},{"paper":null,"slug":"retinal-vessel-segmentation-based-on","title":"Retinal Vessel Segmentation Based on Conditional Deep Convolutional Generative Adversarial Networks","date":"2018-05-11","arxiv_id":"1805.04224","n_code_links":0,"syntology":null},{"paper":null,"slug":"revisiting-dilated-convolution-a-simple-1","title":"Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation","date":"2018-05-11","arxiv_id":"1805.04574","n_code_links":0,"syntology":null},{"paper":null,"slug":"boosting-up-scene-text-detectors-with-guided","title":"Boosting up Scene Text Detectors with Guided CNN","date":"2018-05-10","arxiv_id":"1805.04132","n_code_links":0,"syntology":null},{"paper":null,"slug":"ganax-a-unified-mimd-simd-acceleration-for","title":"GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks","date":"2018-05-10","arxiv_id":"1806.01107","n_code_links":0,"syntology":null},{"paper":null,"slug":"laconic-deep-learning-computing","title":"Laconic Deep Learning Computing","date":"2018-05-10","arxiv_id":"1805.04513","n_code_links":0,"syntology":null},{"paper":null,"slug":"unifying-data-model-and-hybrid-parallelism-in","title":"Unifying Data, Model and Hybrid Parallelism in Deep Learning via Tensor Tiling","date":"2018-05-10","arxiv_id":"1805.04170","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-memristor-based-unsupervised-neuromorphic","title":"A Memristor based Unsupervised Neuromorphic System Towards Fast and Energy-Efficient GAN","date":"2018-05-09","arxiv_id":"1806.01775","n_code_links":0,"syntology":null},{"paper":"/paper/evaluating-resnext-model-architecture-for","slug":"evaluating-resnext-model-architecture-for","title":"Evaluating ResNeXt Model Architecture for Image Classification","date":"2018-05-09","arxiv_id":"1805.08700","n_code_links":1,"syntology":null},{"paper":null,"slug":"fast-and-accurate-tumor-segmentation-of","title":"Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Features","date":"2018-05-09","arxiv_id":"1805.03699","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-performance-evaluation-of-convolutional","title":"A Performance Evaluation of Convolutional Neural Networks for Face Anti Spoofing","date":"2018-05-08","arxiv_id":"1805.04176","n_code_links":0,"syntology":null},{"paper":null,"slug":"fully-automated-segmentation-of","title":"Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images","date":"2018-05-08","arxiv_id":"1805.03278","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-short-cut-connections-for-object","title":"Learning Short-Cut Connections for Object Counting","date":"2018-05-08","arxiv_id":"1805.02919","n_code_links":0,"syntology":null},{"paper":"/paper/moire-photo-restoration-using-multiresolution","slug":"moire-photo-restoration-using-multiresolution","title":"Moiré Photo Restoration Using Multiresolution Convolutional Neural Networks","date":"2018-05-08","arxiv_id":"1805.02996","n_code_links":1,"syntology":null},{"paper":null,"slug":"regan-relaxbarinforce-based-sequence","title":"ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs","date":"2018-05-08","arxiv_id":"1805.02788","n_code_links":0,"syntology":null},{"paper":null,"slug":"superresolution-method-for-data-deconvolution","title":"Superresolution method for data deconvolution by superposition of point sources","date":"2018-05-08","arxiv_id":"1805.03170","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-effectiveness-of-instance-normalization-a","title":"The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing","date":"2018-05-08","arxiv_id":"1805.03305","n_code_links":0,"syntology":null},{"paper":"/paper/detecting-traffic-lights-by-single-shot","slug":"detecting-traffic-lights-by-single-shot","title":"Detecting Traffic Lights by Single Shot Detection","date":"2018-05-07","arxiv_id":"1805.02523","n_code_links":2,"syntology":null},{"paper":null,"slug":"elastic-registration-of-medical-images-with","title":"GAN Based Medical Image Registration","date":"2018-05-07","arxiv_id":"1805.02369","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-optical-flow-via-dilated-networks","title":"Learning Optical Flow via Dilated Networks and Occlusion Reasoning","date":"2018-05-07","arxiv_id":"1805.02733","n_code_links":0,"syntology":null},{"paper":null,"slug":"megan-mixture-of-experts-of-generative","title":"MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation","date":"2018-05-07","arxiv_id":"1805.02481","n_code_links":0,"syntology":null},{"paper":"/paper/unpaired-multi-domain-image-generation-via","slug":"unpaired-multi-domain-image-generation-via","title":"Unpaired Multi-Domain Image Generation via Regularized Conditional GANs","date":"2018-05-07","arxiv_id":"1805.02456","n_code_links":1,"syntology":null},{"paper":null,"slug":"quantization-mimic-towards-very-tiny-cnn-for","title":"Quantization Mimic: Towards Very Tiny CNN for Object Detection","date":"2018-05-06","arxiv_id":"1805.02152","n_code_links":0,"syntology":null},{"paper":null,"slug":"squeezejet-high-level-synthesis-accelerator","title":"SqueezeJet: High-level Synthesis Accelerator Design for Deep Convolutional Neural Networks","date":"2018-05-06","arxiv_id":"1805.08695","n_code_links":0,"syntology":null},{"paper":null,"slug":"fast-converging-conditional-generative","title":"Fast-converging Conditional Generative Adversarial Networks for Image Synthesis","date":"2018-05-05","arxiv_id":"1805.01972","n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-temporal-modeling-for-video-based","slug":"revisiting-temporal-modeling-for-video-based","title":"Revisiting Temporal Modeling for Video-based Person ReID","date":"2018-05-05","arxiv_id":"1805.02104","n_code_links":8,"syntology":null},{"paper":null,"slug":"rifcn-recurrent-network-in-fully","title":"RiFCN: Recurrent Network in Fully Convolutional Network for Semantic Segmentation of High Resolution Remote Sensing Images","date":"2018-05-05","arxiv_id":"1805.02091","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-feature-learning-via-non","slug":"unsupervised-feature-learning-via-non","title":"Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination","date":"2018-05-05","arxiv_id":"1805.01978","n_code_links":15,"syntology":{"ran":19,"of":23,"n_ran_checked":17,"n_instrument":2,"unverified":4,"pointer_only":16,"phrase":"19 ran (of which 14 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["zhirongw/lemniscate.pytorch"],"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/highly-efficient-8-bit-low-precision-1","slug":"highly-efficient-8-bit-low-precision-1","title":"Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe","date":"2018-05-04","arxiv_id":"1805.08691","n_code_links":1,"syntology":null},{"paper":null,"slug":"framewise-approach-in-multimodal-emotion","title":"Framewise approach in multimodal emotion recognition in OMG challenge","date":"2018-05-03","arxiv_id":"1805.01369","n_code_links":0,"syntology":null},{"paper":"/paper/mc-gan-multi-conditional-generative","slug":"mc-gan-multi-conditional-generative","title":"MC-GAN: Multi-conditional Generative Adversarial Network for Image Synthesis","date":"2018-05-03","arxiv_id":"1805.01123","n_code_links":2,"syntology":null},{"paper":null,"slug":"perceptually-optimized-generative-adversarial","title":"Perceptually Optimized Generative Adversarial Network for Single Image Dehazing","date":"2018-05-03","arxiv_id":"1805.01084","n_code_links":0,"syntology":null},{"paper":null,"slug":"sdcnet-a-computation-efficient-cnn-for-object","title":"SdcNet: A Computation-Efficient CNN for Object Recognition","date":"2018-05-03","arxiv_id":"1805.01317","n_code_links":0,"syntology":null},{"paper":null,"slug":"altered-fingerprints-detection-and","title":"Altered Fingerprints: Detection and Localization","date":"2018-05-02","arxiv_id":"1805.00911","n_code_links":0,"syntology":null},{"paper":null,"slug":"boosting-noise-robustness-of-acoustic-model","title":"Boosting Noise Robustness of Acoustic Model via Deep Adversarial Training","date":"2018-05-02","arxiv_id":"1805.01357","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-perm-set-net-learn-to-predict-sets-with","title":"Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks","date":"2018-05-02","arxiv_id":"1805.00613","n_code_links":0,"syntology":null},{"paper":"/paper/evolving-mario-levels-in-the-latent-space-of","slug":"evolving-mario-levels-in-the-latent-space-of","title":"Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network","date":"2018-05-02","arxiv_id":"1805.00728","n_code_links":3,"syntology":null},{"paper":"/paper/exploring-the-limits-of-weakly-supervised","slug":"exploring-the-limits-of-weakly-supervised","title":"Exploring the Limits of Weakly Supervised Pretraining","date":"2018-05-02","arxiv_id":"1805.00932","n_code_links":4,"syntology":null},{"paper":null,"slug":"lidar-cloud-detection-with-fully","title":"Lidar Cloud Detection with Fully Convolutional Networks","date":"2018-05-02","arxiv_id":"1805.00928","n_code_links":0,"syntology":null},{"paper":null,"slug":"text-to-image-synthesis-using-generative","title":"Text to Image Synthesis Using Generative Adversarial Networks","date":"2018-05-02","arxiv_id":"1805.00676","n_code_links":0,"syntology":null},{"paper":null,"slug":"convolutional-neural-network-architectures","title":"Convolutional Neural Network Architectures for Signals Supported on Graphs","date":"2018-05-01","arxiv_id":"1805.00165","n_code_links":0,"syntology":null},{"paper":null,"slug":"object-activity-scene-description","title":"Object Activity Scene Description, Construction and Recognition","date":"2018-05-01","arxiv_id":"1805.00258","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-anti-fraud-system-for-car-insurance-claim","title":"An Anti-fraud System for Car Insurance Claim Based on Visual Evidence","date":"2018-04-30","arxiv_id":"1804.11207","n_code_links":0,"syntology":null},{"paper":null,"slug":"improved-image-captioning-with-adversarial","title":"Adversarial Semantic Alignment for Improved Image Captions","date":"2018-04-30","arxiv_id":"1805.00063","n_code_links":0,"syntology":null},{"paper":"/paper/machine-learning-for-exam-triage","slug":"machine-learning-for-exam-triage","title":"Machine Learning for Exam Triage","date":"2018-04-30","arxiv_id":"1805.00503","n_code_links":1,"syntology":null},{"paper":null,"slug":"stack-u-net-refinement-network-for-image","title":"Stack-U-Net: Refinement Network for Image Segmentation on the Example of Optic Disc and Cup","date":"2018-04-30","arxiv_id":"1804.11294","n_code_links":0,"syntology":null},{"paper":null,"slug":"ultra-power-efficient-cnn-domain-specific","title":"Ultra Power-Efficient CNN Domain Specific Accelerator with 9.3TOPS/Watt for Mobile and Embedded Applications","date":"2018-04-30","arxiv_id":"1805.00361","n_code_links":0,"syntology":null},{"paper":"/paper/an-end-to-end-deep-learning-architecture-for","slug":"an-end-to-end-deep-learning-architecture-for","title":"An End-to-End Deep Learning Architecture for Graph Classification","date":"2018-04-29","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"treesegnet-adaptive-tree-cnns-for","title":"TreeSegNet: Adaptive Tree CNNs for Subdecimeter Aerial Image Segmentation","date":"2018-04-29","arxiv_id":"1804.10879","n_code_links":0,"syntology":null},{"paper":null,"slug":"spiking-deep-residual-network","title":"Spiking Deep Residual Network","date":"2018-04-28","arxiv_id":"1805.01352","n_code_links":0,"syntology":null},{"paper":null,"slug":"human-motion-modeling-using-dvgans","title":"Human Motion Modeling using DVGANs","date":"2018-04-27","arxiv_id":"1804.10652","n_code_links":0,"syntology":null},{"paper":null,"slug":"mapping-road-lanes-using-laser-remission-and","title":"Mapping Road Lanes Using Laser Remission and Deep Neural Networks","date":"2018-04-27","arxiv_id":"1804.10662","n_code_links":0,"syntology":null},{"paper":"/paper/visual-relationship-detection-with-deep","slug":"visual-relationship-detection-with-deep","title":"Visual relationship detection with deep structural ranking","date":"2018-04-27","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/accelerator-aware-pruning-for-convolutional","slug":"accelerator-aware-pruning-for-convolutional","title":"Accelerator-Aware Pruning for Convolutional Neural Networks","date":"2018-04-26","arxiv_id":"1804.09862","n_code_links":3,"syntology":null},{"paper":null,"slug":"iamnn-iterative-and-adaptive-mobile-neural","title":"IamNN: Iterative and Adaptive Mobile Neural Network for Efficient Image Classification","date":"2018-04-26","arxiv_id":"1804.10123","n_code_links":0,"syntology":null},{"paper":null,"slug":"visual-data-synthesis-via-gan-for-zero-shot","title":"Visual Data Synthesis via GAN for Zero-Shot Video Classification","date":"2018-04-26","arxiv_id":"1804.10073","n_code_links":0,"syntology":null},{"paper":"/paper/applying-faster-r-cnn-for-object-detection-on","slug":"applying-faster-r-cnn-for-object-detection-on","title":"Applying Faster R-CNN for Object Detection on Malaria Images","date":"2018-04-25","arxiv_id":"1804.09548","n_code_links":2,"syntology":null},{"paper":"/paper/convolutional-generative-adversarial-networks","slug":"convolutional-generative-adversarial-networks","title":"Convolutional Generative Adversarial Networks with Binary Neurons for Polyphonic Music Generation","date":"2018-04-25","arxiv_id":"1804.09399","n_code_links":3,"syntology":{"ran":19,"of":35,"n_ran_checked":19,"n_instrument":0,"unverified":16,"pointer_only":0,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 0 where Syntology's instrument failed) · 16 unverified","official":{"repos":["salu133445/musegan"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":12,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/learning-a-discriminative-feature-network-for","slug":"learning-a-discriminative-feature-network-for","title":"Learning a Discriminative Feature Network for Semantic Segmentation","date":"2018-04-25","arxiv_id":"1804.09337","n_code_links":3,"syntology":{"ran":3,"of":8,"n_ran_checked":3,"n_instrument":0,"unverified":5,"pointer_only":0,"phrase":"3 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; 0 where Syntology's instrument failed) · 5 unverified","official":null}},{"paper":null,"slug":"multiagent-soft-q-learning","title":"Multiagent Soft Q-Learning","date":"2018-04-25","arxiv_id":"1804.09817","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-anchor-free-region-proposal-network-for","title":"An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches","date":"2018-04-24","arxiv_id":"1804.09003","n_code_links":0,"syntology":null}],"record_sha256":"a30634b2c9f4a3527566c9a25b35f53e60a11955ea465893d3a68ee33c4e50eb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}