{"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/relu/papers/87","list_of":"/method/relu","method":"ReLU","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":87,"pages_in_order":104,"rows_per_page":100,"rows":[8601,8700],"of":10350,"counts":{"archive_papers_tagged":10350,"with_a_code_link":4256,"where_syntology_ran_a_sample":1079,"not_listed_spam_title":0,"listed":10350,"listed_where_code_ran":1079,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":909,"every_run_a_failure_of_syntologys_instrument":170,"listed_with_a_run_with_no_instrument_failure":909,"listed_every_run_a_failure_of_syntologys_instrument":170,"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/relu","prev":"/method/relu/papers/86","next":"/method/relu/papers/88","papers":[{"paper":null,"slug":"neural-source-filter-waveform-models-for","title":"Neural source-filter waveform models for statistical parametric speech synthesis","date":"2019-04-27","arxiv_id":"1904.12088","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-traffic-sign-detection-and","title":"Automatic Traffic Sign Detection and Recognition Using SegU-Net and a Modified Tversky Loss Function With L1-Constraint","date":"2019-04-26","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/on-exact-computation-with-an-infinitely-wide","slug":"on-exact-computation-with-an-infinitely-wide","title":"On Exact Computation with an Infinitely Wide Neural Net","date":"2019-04-26","arxiv_id":"1904.11955","n_code_links":2,"syntology":null},{"paper":"/paper/transformers-with-convolutional-context-for","slug":"transformers-with-convolutional-context-for","title":"Transformers with convolutional context for ASR","date":"2019-04-26","arxiv_id":"1904.11660","n_code_links":4,"syntology":{"ran":8,"of":8,"n_ran_checked":8,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/190411126","slug":"190411126","title":"Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks","date":"2019-04-25","arxiv_id":"1904.11126","n_code_links":1,"syntology":null},{"paper":"/paper/190411486","slug":"190411486","title":"Making Convolutional Networks Shift-Invariant Again","date":"2019-04-25","arxiv_id":"1904.11486","n_code_links":7,"syntology":{"ran":2,"of":4,"n_ran_checked":0,"n_instrument":2,"unverified":2,"pointer_only":3,"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) · 2 unverified","official":{"repos":["adobe/antialiased-cnns"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"paper":null,"slug":"face-video-generation-from-a-single-image-and","title":"Face Video Generation from a Single Image and Landmarks","date":"2019-04-25","arxiv_id":"1904.11521","n_code_links":0,"syntology":null},{"paper":"/paper/gcnet-non-local-networks-meet-squeeze","slug":"gcnet-non-local-networks-meet-squeeze","title":"GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond","date":"2019-04-25","arxiv_id":"1904.11492","n_code_links":9,"syntology":null},{"paper":null,"slug":"improved-visible-to-ir-image-transformation","title":"Improved visible to IR image transformation using synthetic data augmentation with cycle-consistent adversarial networks","date":"2019-04-25","arxiv_id":"1904.11620","n_code_links":0,"syntology":null},{"paper":"/paper/reppoints-point-set-representation-for-object","slug":"reppoints-point-set-representation-for-object","title":"RepPoints: Point Set Representation for Object Detection","date":"2019-04-25","arxiv_id":"1904.11490","n_code_links":6,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":3,"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":["microsoft/RepPoints"],"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":"190411031","title":"Ultrasound segmentation using U-Net: learning from simulated data and testing on real data","date":"2019-04-24","arxiv_id":"1904.11031","n_code_links":0,"syntology":null},{"paper":null,"slug":"layer-dynamics-of-linearised-neural-nets","title":"Layer Dynamics of Linearised Neural Nets","date":"2019-04-24","arxiv_id":"1904.10689","n_code_links":0,"syntology":null},{"paper":null,"slug":"low-memory-neural-network-training-a","title":"Low-Memory Neural Network Training: A Technical Report","date":"2019-04-24","arxiv_id":"1904.10631","n_code_links":0,"syntology":null},{"paper":null,"slug":"prediction-of-progression-to-alzheimers","title":"Prediction of Progression to Alzheimer's disease with Deep InfoMax","date":"2019-04-24","arxiv_id":"1904.10931","n_code_links":0,"syntology":null},{"paper":"/paper/the-vgg-image-annotator-via","slug":"the-vgg-image-annotator-via","title":"The VIA Annotation Software for Images, Audio and Video","date":"2019-04-24","arxiv_id":"1904.10699","n_code_links":1,"syntology":null},{"paper":null,"slug":"towards-combining-on-off-policy-methods-for","title":"Towards Combining On-Off-Policy Methods for Real-World Applications","date":"2019-04-24","arxiv_id":"1904.10642","n_code_links":0,"syntology":null},{"paper":"/paper/attention-guided-network-for-ghost-free-high","slug":"attention-guided-network-for-ghost-free-high","title":"Attention-guided Network for Ghost-free High Dynamic Range Imaging","date":"2019-04-23","arxiv_id":"1904.10293","n_code_links":5,"syntology":null},{"paper":"/paper/chunkflow-distributed-hybrid-cloud-processing","slug":"chunkflow-distributed-hybrid-cloud-processing","title":"Chunkflow: Distributed Hybrid Cloud Processing of Large 3D Images by Convolutional Nets","date":"2019-04-23","arxiv_id":"1904.10489","n_code_links":1,"syntology":null},{"paper":"/paper/lung-nodule-classification-using-deep-local","slug":"lung-nodule-classification-using-deep-local","title":"Lung Nodule Classification using Deep Local-Global Networks","date":"2019-04-23","arxiv_id":"1904.10126","n_code_links":1,"syntology":null},{"paper":"/paper/190409925","slug":"190409925","title":"Attention Augmented Convolutional Networks","date":"2019-04-22","arxiv_id":"1904.09925","n_code_links":14,"syntology":{"ran":3,"of":6,"n_ran_checked":1,"n_instrument":2,"unverified":3,"pointer_only":1,"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) · 3 unverified","official":null}},{"paper":"/paper/190501969","slug":"190501969","title":"Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring","date":"2019-04-22","arxiv_id":"1905.01969","n_code_links":7,"syntology":null},{"paper":"/paper/an-energy-and-gpu-computation-efficient","slug":"an-energy-and-gpu-computation-efficient","title":"An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection","date":"2019-04-22","arxiv_id":"1904.09730","n_code_links":12,"syntology":null},{"paper":null,"slug":"190412613","title":"State Classification of Cooking Objects Using a VGG CNN","date":"2019-04-21","arxiv_id":"1904.12613","n_code_links":0,"syntology":null},{"paper":"/paper/dynamic-past-and-future-for-neural-machine","slug":"dynamic-past-and-future-for-neural-machine","title":"Dynamic Past and Future for Neural Machine Translation","date":"2019-04-21","arxiv_id":"1904.09646","n_code_links":1,"syntology":null},{"paper":"/paper/190409380","slug":"190409380","title":"Repurposing Entailment for Multi-Hop Question Answering Tasks","date":"2019-04-20","arxiv_id":"1904.09380","n_code_links":4,"syntology":null},{"paper":"/paper/190409408","slug":"190409408","title":"Language Models with Transformers","date":"2019-04-20","arxiv_id":"1904.09408","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"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) · 0 unverified","official":{"repos":["cgraywang/gluon-nlp-1"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"190409410","title":"LEARNet Dynamic Imaging Network for Micro Expression Recognition","date":"2019-04-20","arxiv_id":"1904.09410","n_code_links":0,"syntology":null},{"paper":"/paper/190409460","slug":"190409460","title":"Data-Driven Neuron Allocation for Scale Aggregation Networks","date":"2019-04-20","arxiv_id":"1904.09460","n_code_links":1,"syntology":null},{"paper":null,"slug":"facial-feature-embedded-cyclegan-for-vis-nir","title":"Facial Feature Embedded CycleGAN for VIS-NIR Translation","date":"2019-04-20","arxiv_id":"1904.09464","n_code_links":0,"syntology":null},{"paper":"/paper/social-ways-learning-multi-modal","slug":"social-ways-learning-multi-modal","title":"Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs","date":"2019-04-20","arxiv_id":"1904.09507","n_code_links":1,"syntology":null},{"paper":"/paper/190409324","slug":"190409324","title":"Mask-Predict: Parallel Decoding of Conditional Masked Language Models","date":"2019-04-19","arxiv_id":"1904.09324","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"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":{"repos":["facebookresearch/Mask-Predict"],"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":"advanced-deep-convolutional-neural-network","title":"Advanced Deep Convolutional Neural Network Approaches for Digital Pathology Image Analysis: a comprehensive evaluation with different use cases","date":"2019-04-19","arxiv_id":"1904.09075","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-q-learning-driven-ct-pancreas","title":"Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net","date":"2019-04-19","arxiv_id":"1904.09120","n_code_links":0,"syntology":null},{"paper":null,"slug":"implicit-regularization-for-deep-neural","title":"Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process","date":"2019-04-19","arxiv_id":"1904.09080","n_code_links":0,"syntology":null},{"paper":"/paper/190408900","slug":"190408900","title":"CornerNet-Lite: Efficient Keypoint Based Object Detection","date":"2019-04-18","arxiv_id":"1904.08900","n_code_links":6,"syntology":{"ran":21,"of":27,"n_ran_checked":21,"n_instrument":0,"unverified":6,"pointer_only":0,"phrase":"21 ran (of which 0 constructed an object rather than computing a result; 21 with no instrument failure: 1 honoured, 0 violated, 20 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":{"repos":["princeton-vl/CornerNet-Lite"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/190413216","slug":"190413216","title":"Signal2Image Modules in Deep Neural Networks for EEG Classification","date":"2019-04-18","arxiv_id":"1904.13216","n_code_links":1,"syntology":null},{"paper":"/paper/cascaded-partial-decoder-for-fast-and","slug":"cascaded-partial-decoder-for-fast-and","title":"Cascaded Partial Decoder for Fast and Accurate Salient Object Detection","date":"2019-04-18","arxiv_id":"1904.08739","n_code_links":1,"syntology":null},{"paper":"/paper/deep-residual-auto-encoders-for-expectation","slug":"deep-residual-auto-encoders-for-expectation","title":"Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary Learning","date":"2019-04-18","arxiv_id":"1904.08827","n_code_links":1,"syntology":null},{"paper":null,"slug":"examining-the-capability-of-gans-to-replace","title":"Examining the Capability of GANs to Replace Real Biomedical Images in Classification Models Training","date":"2019-04-18","arxiv_id":"1904.08688","n_code_links":0,"syntology":null},{"paper":"/paper/real-time-style-transfer-with-strength","slug":"real-time-style-transfer-with-strength","title":"Real-Time Style Transfer With Strength Control","date":"2019-04-18","arxiv_id":"1904.08643","n_code_links":1,"syntology":null},{"paper":"/paper/centernet-object-detection-with-keypoint","slug":"centernet-object-detection-with-keypoint","title":"CenterNet: Keypoint Triplets for Object Detection","date":"2019-04-17","arxiv_id":"1904.08189","n_code_links":20,"syntology":{"ran":2,"of":11,"n_ran_checked":0,"n_instrument":2,"unverified":9,"pointer_only":4,"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) · 9 unverified","official":{"repos":["Duankaiwen/CenterNet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"denet-a-universal-network-for-counting-crowd","title":"DENet: A Universal Network for Counting Crowd with Varying Densities and Scales","date":"2019-04-17","arxiv_id":"1904.08056","n_code_links":0,"syntology":null},{"paper":"/paper/do-lateral-views-help-automated-chest-x-ray","slug":"do-lateral-views-help-automated-chest-x-ray","title":"Do Lateral Views Help Automated Chest X-ray Predictions?","date":"2019-04-17","arxiv_id":"1904.08534","n_code_links":1,"syntology":null},{"paper":null,"slug":"use-net-incorporating-squeeze-and-excitation","title":"USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets","date":"2019-04-17","arxiv_id":"1904.08254","n_code_links":0,"syntology":null},{"paper":"/paper/learning-pyramid-context-encoder-network-for","slug":"learning-pyramid-context-encoder-network-for","title":"Learning Pyramid-Context Encoder Network for High-Quality Image Inpainting","date":"2019-04-16","arxiv_id":"1904.07475","n_code_links":2,"syntology":null},{"paper":"/paper/nas-fpn-learning-scalable-feature-pyramid","slug":"nas-fpn-learning-scalable-feature-pyramid","title":"NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection","date":"2019-04-16","arxiv_id":"1904.07392","n_code_links":8,"syntology":null},{"paper":null,"slug":"on-the-mathematical-understanding-of-resnet","title":"On the Mathematical Understanding of ResNet with Feynman Path Integral","date":"2019-04-16","arxiv_id":"1904.07568","n_code_links":0,"syntology":null},{"paper":null,"slug":"suction-grasp-region-prediction-using-self","title":"Suction Grasp Region Prediction using Self-supervised Learning for Object Picking in Dense Clutter","date":"2019-04-16","arxiv_id":"1904.07402","n_code_links":0,"syntology":null},{"paper":null,"slug":"swtvm-exploring-the-automated-compilation-for","title":"swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor","date":"2019-04-16","arxiv_id":"1904.07404","n_code_links":0,"syntology":null},{"paper":null,"slug":"understanding-the-behaviors-of-bert-in","title":"Understanding the Behaviors of BERT in Ranking","date":"2019-04-16","arxiv_id":"1904.07531","n_code_links":0,"syntology":null},{"paper":null,"slug":"characterizing-the-variability-in-face","title":"Characterizing the Variability in Face Recognition Accuracy Relative to Race","date":"2019-04-15","arxiv_id":"1904.07325","n_code_links":0,"syntology":null},{"paper":"/paper/improved-precision-and-recall-metric-for","slug":"improved-precision-and-recall-metric-for","title":"Improved Precision and Recall Metric for Assessing Generative Models","date":"2019-04-15","arxiv_id":"1904.06991","n_code_links":10,"syntology":{"ran":30,"of":42,"n_ran_checked":20,"n_instrument":10,"unverified":12,"pointer_only":25,"phrase":"30 ran (of which 12 constructed an object rather than computing a result; 20 with no instrument failure: 5 honoured, 1 violated, 14 with no contract checked; 10 where Syntology's instrument failed) · 12 unverified","official":{"repos":["kynkaat/improved-precision-and-recall-metric"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/personalized-context-aware-re-ranking-for-e","slug":"personalized-context-aware-re-ranking-for-e","title":"Personalized Re-ranking for Recommendation","date":"2019-04-15","arxiv_id":"1904.06813","n_code_links":1,"syntology":null},{"paper":"/paper/rethinking-classification-and-localization-in","slug":"rethinking-classification-and-localization-in","title":"Rethinking Classification and Localization for Object Detection","date":"2019-04-13","arxiv_id":"1904.06493","n_code_links":2,"syntology":null},{"paper":"/paper/uni-em-an-environment-for-deep-neural-network","slug":"uni-em-an-environment-for-deep-neural-network","title":"UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images","date":"2019-04-12","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/unsupervised-speech-domain-adaptation-based","slug":"unsupervised-speech-domain-adaptation-based","title":"Unsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech Recognition","date":"2019-04-12","arxiv_id":"1904.06086","n_code_links":1,"syntology":null},{"paper":"/paper/an-empirical-study-of-spatial-attention","slug":"an-empirical-study-of-spatial-attention","title":"An Empirical Study of Spatial Attention Mechanisms in Deep Networks","date":"2019-04-11","arxiv_id":"1904.05873","n_code_links":1,"syntology":null},{"paper":"/paper/compressing-deep-neural-networks-by-matrix","slug":"compressing-deep-neural-networks-by-matrix","title":"Compressing deep neural networks by matrix product operators","date":"2019-04-11","arxiv_id":"1904.06194","n_code_links":1,"syntology":{"ran":2,"of":5,"n_ran_checked":1,"n_instrument":1,"unverified":3,"pointer_only":1,"phrase":"2 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; 1 where Syntology's instrument failed) · 3 unverified","official":{"repos":["zfgao66/deeplearning-mpo"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"retinal-vessels-segmentation-based-on-dilated","title":"Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural Network","date":"2019-04-11","arxiv_id":"1904.05644","n_code_links":0,"syntology":null},{"paper":null,"slug":"190406197","title":"Simulation of hyperelastic materials in real-time using Deep Learning","date":"2019-04-10","arxiv_id":"1904.06197","n_code_links":0,"syntology":null},{"paper":null,"slug":"190408505","title":"Dynamic Gesture Recognition by Using CNNs and Star RGB: a Temporal Information Condensation","date":"2019-04-10","arxiv_id":"1904.08505","n_code_links":0,"syntology":null},{"paper":"/paper/c3ae-exploring-the-limits-of-compact-model","slug":"c3ae-exploring-the-limits-of-compact-model","title":"C3AE: Exploring the Limits of Compact Model for Age Estimation","date":"2019-04-10","arxiv_id":"1904.05059","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-inversion-of-electrical","title":"Deep Learning Inversion of Electrical Resistivity Data","date":"2019-04-10","arxiv_id":"1904.05265","n_code_links":0,"syntology":null},{"paper":"/paper/drop-an-octave-reducing-spatial-redundancy-in","slug":"drop-an-octave-reducing-spatial-redundancy-in","title":"Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution","date":"2019-04-10","arxiv_id":"1904.05049","n_code_links":28,"syntology":{"ran":24,"of":34,"n_ran_checked":11,"n_instrument":13,"unverified":10,"pointer_only":9,"phrase":"24 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 13 where Syntology's instrument failed) · 10 unverified","official":{"repos":["facebookresearch/OctConv"],"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":"dsnet-an-efficient-cnn-for-road-scene","title":"DSNet: An Efficient CNN for Road Scene Segmentation","date":"2019-04-10","arxiv_id":"1904.05022","n_code_links":0,"syntology":null},{"paper":null,"slug":"evaluation-of-a-dual-convolutional-neural","title":"Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery","date":"2019-04-10","arxiv_id":"1904.05304","n_code_links":0,"syntology":null},{"paper":"/paper/soft-conditional-computation","slug":"soft-conditional-computation","title":"CondConv: Conditionally Parameterized Convolutions for Efficient Inference","date":"2019-04-10","arxiv_id":"1904.04971","n_code_links":9,"syntology":null},{"paper":"/paper/weakly-supervised-learning-of-instance","slug":"weakly-supervised-learning-of-instance","title":"Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations","date":"2019-04-10","arxiv_id":"1904.05044","n_code_links":8,"syntology":{"ran":9,"of":10,"n_ran_checked":8,"n_instrument":1,"unverified":1,"pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"a-new-gan-based-end-to-end-tts-training","title":"A New GAN-based End-to-End TTS Training Algorithm","date":"2019-04-09","arxiv_id":"1904.04775","n_code_links":0,"syntology":null},{"paper":null,"slug":"approximation-in-lp-with-deep-relu-neural","title":"Approximation in $L^p(μ)$ with deep ReLU neural networks","date":"2019-04-09","arxiv_id":"1904.04789","n_code_links":0,"syntology":null},{"paper":"/paper/cyclegan-vc2-improved-cyclegan-based-non","slug":"cyclegan-vc2-improved-cyclegan-based-non","title":"CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion","date":"2019-04-09","arxiv_id":"1904.04631","n_code_links":6,"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":"audio-source-separation-via-multi-scale","title":"Audio Source Separation via Multi-Scale Learning with Dilated Dense U-Nets","date":"2019-04-08","arxiv_id":"1904.04161","n_code_links":0,"syntology":null},{"paper":"/paper/foveabox-beyond-anchor-based-object-detector","slug":"foveabox-beyond-anchor-based-object-detector","title":"FoveaBox: Beyond Anchor-based Object Detector","date":"2019-04-08","arxiv_id":"1904.03797","n_code_links":7,"syntology":null},{"paper":"/paper/scsampler-sampling-salient-clips-from-video","slug":"scsampler-sampling-salient-clips-from-video","title":"SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition","date":"2019-04-08","arxiv_id":"1904.04289","n_code_links":0,"syntology":null},{"paper":"/paper/temporal-convolution-for-real-time-keyword","slug":"temporal-convolution-for-real-time-keyword","title":"Temporal Convolution for Real-time Keyword Spotting on Mobile Devices","date":"2019-04-08","arxiv_id":"1904.03814","n_code_links":3,"syntology":null},{"paper":null,"slug":"unsupervised-feature-learning-for","title":"Unsupervised Feature Learning for Environmental Sound Classification Using Weighted Cycle-Consistent Generative Adversarial Network","date":"2019-04-08","arxiv_id":"1904.04221","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-nms-refining-pedestrian-detection-in","slug":"adaptive-nms-refining-pedestrian-detection-in","title":"Adaptive NMS: Refining Pedestrian Detection in a Crowd","date":"2019-04-07","arxiv_id":"1904.03629","n_code_links":0,"syntology":null},{"paper":"/paper/adaptively-connected-neural-networks","slug":"adaptively-connected-neural-networks","title":"Adaptively Connected Neural Networks","date":"2019-04-07","arxiv_id":"1904.03579","n_code_links":1,"syntology":null},{"paper":null,"slug":"identity-preserving-face-recovery-from-1","title":"Identity-preserving Face Recovery from Stylized Portraits","date":"2019-04-07","arxiv_id":"1904.04241","n_code_links":0,"syntology":null},{"paper":"/paper/jumprelu-a-retrofit-defense-strategy-for","slug":"jumprelu-a-retrofit-defense-strategy-for","title":"JumpReLU: A Retrofit Defense Strategy for Adversarial Attacks","date":"2019-04-07","arxiv_id":"1904.03750","n_code_links":1,"syntology":null},{"paper":"/paper/3d-dilated-multi-fiber-network-for-real-time","slug":"3d-dilated-multi-fiber-network-for-real-time","title":"3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI","date":"2019-04-06","arxiv_id":"1904.03355","n_code_links":1,"syntology":null},{"paper":null,"slug":"taco-vc-a-single-speaker-tacotron-based-voice","title":"Taco-VC: A Single Speaker Tacotron based Voice Conversion with Limited Data","date":"2019-04-06","arxiv_id":"1904.03522","n_code_links":0,"syntology":null},{"paper":null,"slug":"thisiscompetition-at-semeval-2019-task-9-bert","title":"ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples","date":"2019-04-06","arxiv_id":"1904.03339","n_code_links":0,"syntology":null},{"paper":"/paper/token-level-ensemble-distillation-for","slug":"token-level-ensemble-distillation-for","title":"Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion","date":"2019-04-06","arxiv_id":"1904.03446","n_code_links":0,"syntology":null},{"paper":null,"slug":"3dq-compact-quantized-neural-networks-for","title":"3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation","date":"2019-04-05","arxiv_id":"1904.03110","n_code_links":0,"syntology":null},{"paper":"/paper/high-level-semantic-feature-detectiona-new","slug":"high-level-semantic-feature-detectiona-new","title":"Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection","date":"2019-04-05","arxiv_id":"1904.02948","n_code_links":2,"syntology":null},{"paper":"/paper/jasper-an-end-to-end-convolutional-neural","slug":"jasper-an-end-to-end-convolutional-neural","title":"Jasper: An End-to-End Convolutional Neural Acoustic Model","date":"2019-04-05","arxiv_id":"1904.03288","n_code_links":10,"syntology":null},{"paper":null,"slug":"modeling-recurrence-for-transformer","title":"Modeling Recurrence for Transformer","date":"2019-04-05","arxiv_id":"1904.03092","n_code_links":0,"syntology":null},{"paper":null,"slug":"revealing-scenes-by-inverting-structure-from","title":"Revealing Scenes by Inverting Structure from Motion Reconstructions","date":"2019-04-05","arxiv_id":"1904.03303","n_code_links":0,"syntology":null},{"paper":"/paper/shapemask-learning-to-segment-novel-objects","slug":"shapemask-learning-to-segment-novel-objects","title":"ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors","date":"2019-04-05","arxiv_id":"1904.03239","n_code_links":1,"syntology":null},{"paper":null,"slug":"an-end-to-end-baseline-for-video-captioning","title":"End-to-End Video Captioning","date":"2019-04-04","arxiv_id":"1904.02628","n_code_links":0,"syntology":null},{"paper":"/paper/libra-r-cnn-towards-balanced-learning-for","slug":"libra-r-cnn-towards-balanced-learning-for","title":"Libra R-CNN: Towards Balanced Learning for Object Detection","date":"2019-04-04","arxiv_id":"1904.02701","n_code_links":6,"syntology":null},{"paper":"/paper/modified-distribution-alignment-for-domain","slug":"modified-distribution-alignment-for-domain","title":"Modified Distribution Alignment for Domain Adaptation with Pre-trained Inception ResNet","date":"2019-04-04","arxiv_id":"1904.02322","n_code_links":2,"syntology":null},{"paper":null,"slug":"multi-reference-tacotron-by-intercross","title":"Multi-reference Tacotron by Intercross Training for Style Disentangling,Transfer and Control in Speech Synthesis","date":"2019-04-04","arxiv_id":"1904.02373","n_code_links":0,"syntology":null},{"paper":null,"slug":"segmentation-of-the-prostatic-gland-and-the","title":"Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic MRI Using Mask-RCNN","date":"2019-04-04","arxiv_id":"1904.02575","n_code_links":0,"syntology":null},{"paper":null,"slug":"uu-nets-connecting-discriminator-and","title":"UU-Nets Connecting Discriminator and Generator for Image to Image Translation","date":"2019-04-04","arxiv_id":"1904.02675","n_code_links":0,"syntology":null},{"paper":"/paper/yolact-real-time-instance-segmentation","slug":"yolact-real-time-instance-segmentation","title":"YOLACT: Real-time Instance Segmentation","date":"2019-04-04","arxiv_id":"1904.02689","n_code_links":48,"syntology":{"ran":18,"of":21,"n_ran_checked":13,"n_instrument":5,"unverified":3,"pointer_only":9,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 2 honoured, 1 violated, 10 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","official":{"repos":["dbolya/yolact"],"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/a-comprehensive-overhaul-of-feature","slug":"a-comprehensive-overhaul-of-feature","title":"A Comprehensive Overhaul of Feature Distillation","date":"2019-04-03","arxiv_id":"1904.01866","n_code_links":2,"syntology":{"ran":4,"of":6,"n_ran_checked":3,"n_instrument":1,"unverified":2,"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) · 2 unverified","official":{"repos":["clovaai/overhaul-distillation"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"hybrid-cosine-based-convolutional-neural","title":"Hybrid Cosine Based Convolutional Neural Networks","date":"2019-04-03","arxiv_id":"1904.01987","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-randomly-wired-neural-networks-for","slug":"exploring-randomly-wired-neural-networks-for","title":"Exploring Randomly Wired Neural Networks for Image Recognition","date":"2019-04-02","arxiv_id":"1904.01569","n_code_links":9,"syntology":{"ran":10,"of":31,"n_ran_checked":10,"n_instrument":0,"unverified":21,"pointer_only":1,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 21 unverified","official":null}}],"record_sha256":"56a2209a62ddb67c3c3cce53045715ea2eb046f691d34231f4900fecd83077da","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}