{"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/batch-normalization/papers/62","list_of":"/method/batch-normalization","method":"Batch Normalization","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":62,"pages_in_order":63,"rows_per_page":100,"rows":[6101,6200],"of":6287,"counts":{"archive_papers_tagged":6287,"with_a_code_link":2771,"where_syntology_ran_a_sample":742,"not_listed_spam_title":0,"listed":6287,"listed_where_code_ran":742,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":627,"every_run_a_failure_of_syntologys_instrument":115,"listed_with_a_run_with_no_instrument_failure":627,"listed_every_run_a_failure_of_syntologys_instrument":115,"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/batch-normalization","prev":"/method/batch-normalization/papers/61","next":"/method/batch-normalization/papers/63","papers":[{"paper":null,"slug":"an-improved-neural-segmentation-method-based","title":"An Improved Neural Segmentation Method Based on U-NET","date":"2017-08-16","arxiv_id":"1708.04747","n_code_links":0,"syntology":null},{"paper":"/paper/improved-regularization-of-convolutional","slug":"improved-regularization-of-convolutional","title":"Improved Regularization of Convolutional Neural Networks with Cutout","date":"2017-08-15","arxiv_id":"1708.04552","n_code_links":28,"syntology":{"ran":21,"of":24,"n_ran_checked":9,"n_instrument":12,"unverified":3,"pointer_only":5,"phrase":"21 ran (of which 3 constructed an object rather than computing a result; 9 with no instrument failure: 3 honoured, 3 violated, 3 with no contract checked; 12 where Syntology's instrument failed) · 3 unverified","official":{"repos":["uoguelph-mlrg/Cutout"],"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":"an-elu-network-with-total-variation-for-image","title":"An ELU Network with Total Variation for Image Denoising","date":"2017-08-14","arxiv_id":"1708.04317","n_code_links":0,"syntology":null},{"paper":"/paper/context-based-normalization-of-histological","slug":"context-based-normalization-of-histological","title":"Context-based Normalization of Histological Stains using Deep Convolutional Features","date":"2017-08-14","arxiv_id":"1708.04099","n_code_links":1,"syntology":null},{"paper":"/paper/revisiting-the-effectiveness-of-off-the-shelf","slug":"revisiting-the-effectiveness-of-off-the-shelf","title":"Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification","date":"2017-08-12","arxiv_id":"1708.03805","n_code_links":0,"syntology":null},{"paper":null,"slug":"unsupervised-incremental-learning-of-deep","title":"Unsupervised Incremental Learning of Deep Descriptors From Video Streams","date":"2017-08-11","arxiv_id":"1708.03615","n_code_links":0,"syntology":null},{"paper":"/paper/joint-multi-person-pose-estimation-and","slug":"joint-multi-person-pose-estimation-and","title":"Joint Multi-Person Pose Estimation and Semantic Part Segmentation","date":"2017-08-10","arxiv_id":"1708.03383","n_code_links":0,"syntology":null},{"paper":null,"slug":"statistics-of-deep-generated-images","title":"Statistics of Deep Generated Images","date":"2017-08-09","arxiv_id":"1708.02688","n_code_links":0,"syntology":null},{"paper":"/paper/fast-scene-understanding-for-autonomous","slug":"fast-scene-understanding-for-autonomous","title":"Fast Scene Understanding for Autonomous Driving","date":"2017-08-08","arxiv_id":"1708.02550","n_code_links":1,"syntology":null},{"paper":"/paper/focal-loss-for-dense-object-detection","slug":"focal-loss-for-dense-object-detection","title":"Focal Loss for Dense Object Detection","date":"2017-08-07","arxiv_id":"1708.02002","n_code_links":234,"syntology":{"ran":11,"of":11,"n_ran_checked":2,"n_instrument":9,"unverified":0,"pointer_only":6,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 9 where Syntology's instrument failed) · 0 unverified","official":{"repos":["facebookresearch/detectron"],"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":"powerai-ddl","title":"PowerAI DDL","date":"2017-08-07","arxiv_id":"1708.02188","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-neural-network-classifiers-with-low","title":"Learning Neural Network Classifiers with Low Model Complexity","date":"2017-07-31","arxiv_id":"1707.09933","n_code_links":0,"syntology":null},{"paper":null,"slug":"streaming-architecture-for-large-scale","title":"Streaming Architecture for Large-Scale Quantized Neural Networks on an FPGA-Based Dataflow Platform","date":"2017-07-31","arxiv_id":"1708.00052","n_code_links":0,"syntology":null},{"paper":"/paper/deep-residual-learning-for-weakly-supervised","slug":"deep-residual-learning-for-weakly-supervised","title":"Deep Residual Learning for Weakly-Supervised Relation Extraction","date":"2017-07-27","arxiv_id":"1707.08866","n_code_links":1,"syntology":null},{"paper":"/paper/leveraging-demonstrations-for-deep","slug":"leveraging-demonstrations-for-deep","title":"Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards","date":"2017-07-27","arxiv_id":"1707.08817","n_code_links":4,"syntology":null},{"paper":null,"slug":"tensor-regression-networks","title":"Tensor Regression Networks","date":"2017-07-26","arxiv_id":"1707.08308","n_code_links":0,"syntology":null},{"paper":"/paper/learning-transferable-architectures-for","slug":"learning-transferable-architectures-for","title":"Learning Transferable Architectures for Scalable Image Recognition","date":"2017-07-21","arxiv_id":"1707.07012","n_code_links":17,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":null}},{"paper":"/paper/memory-efficient-implementation-of-densenets","slug":"memory-efficient-implementation-of-densenets","title":"Memory-Efficient Implementation of DenseNets","date":"2017-07-21","arxiv_id":"1707.06990","n_code_links":6,"syntology":null},{"paper":"/paper/the-inaturalist-species-classification-and","slug":"the-inaturalist-species-classification-and","title":"The iNaturalist Species Classification and Detection Dataset","date":"2017-07-20","arxiv_id":"1707.06642","n_code_links":21,"syntology":null},{"paper":"/paper/channel-pruning-for-accelerating-very-deep","slug":"channel-pruning-for-accelerating-very-deep","title":"Channel Pruning for Accelerating Very Deep Neural Networks","date":"2017-07-19","arxiv_id":"1707.06168","n_code_links":1,"syntology":null},{"paper":"/paper/efficient-architecture-search-by-network","slug":"efficient-architecture-search-by-network","title":"Efficient Architecture Search by Network Transformation","date":"2017-07-16","arxiv_id":"1707.04873","n_code_links":3,"syntology":null},{"paper":null,"slug":"generative-adversarial-network-based-on","title":"Generative Adversarial Network based on Resnet for Conditional Image Restoration","date":"2017-07-16","arxiv_id":"1707.04881","n_code_links":0,"syntology":null},{"paper":null,"slug":"do-convolutional-networks-need-to-be-deep-for","title":"Do Convolutional Networks need to be Deep for Text Classification ?","date":"2017-07-13","arxiv_id":"1707.04108","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-intentional-unintentional-agent-learning","title":"The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously","date":"2017-07-11","arxiv_id":"1707.03300","n_code_links":0,"syntology":null},{"paper":"/paper/learning-visual-reasoning-without-strong","slug":"learning-visual-reasoning-without-strong","title":"Learning Visual Reasoning Without Strong Priors","date":"2017-07-10","arxiv_id":"1707.03017","n_code_links":2,"syntology":null},{"paper":"/paper/revisiting-unreasonable-effectiveness-of-data","slug":"revisiting-unreasonable-effectiveness-of-data","title":"Revisiting Unreasonable Effectiveness of Data in Deep Learning Era","date":"2017-07-10","arxiv_id":"1707.02968","n_code_links":2,"syntology":null},{"paper":"/paper/elf-an-extensive-lightweight-and-flexible","slug":"elf-an-extensive-lightweight-and-flexible","title":"ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games","date":"2017-07-04","arxiv_id":"1707.01067","n_code_links":2,"syntology":null},{"paper":"/paper/shufflenet-an-extremely-efficient","slug":"shufflenet-an-extremely-efficient","title":"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices","date":"2017-07-04","arxiv_id":"1707.01083","n_code_links":38,"syntology":null},{"paper":"/paper/modulating-early-visual-processing-by","slug":"modulating-early-visual-processing-by","title":"Modulating early visual processing by language","date":"2017-07-02","arxiv_id":"1707.00683","n_code_links":2,"syntology":null},{"paper":"/paper/temporal-residual-networks-for-dynamic-scene","slug":"temporal-residual-networks-for-dynamic-scene","title":"Temporal Residual Networks for Dynamic Scene Recognition","date":"2017-07-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/multi-scale-multi-band-densenets-for-audio","slug":"multi-scale-multi-band-densenets-for-audio","title":"Multi-scale Multi-band DenseNets for Audio Source Separation","date":"2017-06-29","arxiv_id":"1706.09588","n_code_links":5,"syntology":null},{"paper":null,"slug":"yes-net-an-effective-detector-based-on-global","title":"Yes-Net: An effective Detector Based on Global Information","date":"2017-06-28","arxiv_id":"1706.09180","n_code_links":0,"syntology":null},{"paper":null,"slug":"recurrent-residual-learning-for-action","title":"Recurrent Residual Learning for Action Recognition","date":"2017-06-27","arxiv_id":"1706.08807","n_code_links":0,"syntology":null},{"paper":"/paper/gans-trained-by-a-two-time-scale-update-rule","slug":"gans-trained-by-a-two-time-scale-update-rule","title":"GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium","date":"2017-06-26","arxiv_id":"1706.08500","n_code_links":71,"syntology":{"ran":3,"of":4,"n_ran_checked":0,"n_instrument":3,"unverified":1,"pointer_only":1,"phrase":"3 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; 3 where Syntology's instrument failed) · 1 unverified","official":{"repos":["bioinf-jku/TTUR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/sampling-matters-in-deep-embedding-learning","slug":"sampling-matters-in-deep-embedding-learning","title":"Sampling Matters in Deep Embedding Learning","date":"2017-06-23","arxiv_id":"1706.07567","n_code_links":6,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/rethinking-atrous-convolution-for-semantic","slug":"rethinking-atrous-convolution-for-semantic","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","date":"2017-06-17","arxiv_id":"1706.05587","n_code_links":77,"syntology":{"ran":3,"of":7,"n_ran_checked":2,"n_instrument":1,"unverified":4,"pointer_only":3,"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) · 4 unverified","official":null}},{"paper":null,"slug":"face-clustering-representation-and-pairwise","title":"Face Clustering: Representation and Pairwise Constraints","date":"2017-06-15","arxiv_id":"1706.05067","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-deep-resnet-blocks-sequentially","title":"Learning Deep ResNet Blocks Sequentially using Boosting Theory","date":"2017-06-15","arxiv_id":"1706.04964","n_code_links":0,"syntology":null},{"paper":null,"slug":"sep-nets-small-and-effective-pattern-networks","title":"SEP-Nets: Small and Effective Pattern Networks","date":"2017-06-13","arxiv_id":"1706.03912","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-the-reliability-of-out-of","slug":"enhancing-the-reliability-of-out-of","title":"Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks","date":"2017-06-08","arxiv_id":"1706.02690","n_code_links":9,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["facebookresearch/odin"],"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/self-normalizing-neural-networks","slug":"self-normalizing-neural-networks","title":"Self-Normalizing Neural Networks","date":"2017-06-08","arxiv_id":"1706.02515","n_code_links":13,"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":{"repos":["bioinf-jku/SNNs"],"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/multi-agent-actor-critic-for-mixed","slug":"multi-agent-actor-critic-for-mixed","title":"Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments","date":"2017-06-07","arxiv_id":"1706.02275","n_code_links":86,"syntology":{"ran":75,"of":143,"n_ran_checked":68,"n_instrument":7,"unverified":68,"pointer_only":99,"phrase":"75 ran (of which 54 constructed an object rather than computing a result; 68 with no instrument failure: 2 honoured, 0 violated, 66 with no contract checked; 7 where Syntology's instrument failed) · 68 unverified","official":{"repos":["openai/multiagent-particle-envs"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"paper":"/paper/parameter-space-noise-for-exploration","slug":"parameter-space-noise-for-exploration","title":"Parameter Space Noise for Exploration","date":"2017-06-06","arxiv_id":"1706.01905","n_code_links":10,"syntology":{"ran":4,"of":5,"n_ran_checked":2,"n_instrument":2,"unverified":1,"pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","official":null}},{"paper":null,"slug":"segmentation-of-intracranial-arterial","title":"Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks","date":"2017-06-04","arxiv_id":"1706.01148","n_code_links":0,"syntology":null},{"paper":"/paper/sar-image-despeckling-using-a-convolutional","slug":"sar-image-despeckling-using-a-convolutional","title":"SAR Image Despeckling Using a Convolutional Neural Network","date":"2017-06-02","arxiv_id":"1706.00552","n_code_links":3,"syntology":null},{"paper":"/paper/diracnets-training-very-deep-neural-networks","slug":"diracnets-training-very-deep-neural-networks","title":"DiracNets: Training Very Deep Neural Networks Without Skip-Connections","date":"2017-06-01","arxiv_id":"1706.00388","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["szagoruyko/diracnets"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/discriminative-k-shot-learning-using","slug":"discriminative-k-shot-learning-using","title":"Discriminative k-shot learning using probabilistic models","date":"2017-06-01","arxiv_id":"1706.00326","n_code_links":0,"syntology":null},{"paper":"/paper/deep-generative-adversarial-networks-for","slug":"deep-generative-adversarial-networks-for","title":"Deep Generative Adversarial Networks for Compressed Sensing Automates MRI","date":"2017-05-31","arxiv_id":"1706.00051","n_code_links":2,"syntology":null},{"paper":"/paper/learning-timememory-efficient-deep","slug":"learning-timememory-efficient-deep","title":"Learning Time/Memory-Efficient Deep Architectures with Budgeted Super Networks","date":"2017-05-31","arxiv_id":"1706.00046","n_code_links":1,"syntology":null},{"paper":"/paper/rsi-cb-a-large-scale-remote-sensing-image","slug":"rsi-cb-a-large-scale-remote-sensing-image","title":"RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data","date":"2017-05-30","arxiv_id":"1705.10450","n_code_links":1,"syntology":null},{"paper":null,"slug":"attribute-guided-face-generation-using","title":"Attribute-Guided Face Generation Using Conditional CycleGAN","date":"2017-05-28","arxiv_id":"1705.09966","n_code_links":0,"syntology":null},{"paper":"/paper/casenet-deep-category-aware-semantic-edge","slug":"casenet-deep-category-aware-semantic-edge","title":"CASENet: Deep Category-Aware Semantic Edge Detection","date":"2017-05-27","arxiv_id":"1705.09759","n_code_links":11,"syntology":{"ran":3,"of":7,"n_ran_checked":1,"n_instrument":2,"unverified":4,"pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","official":null}},{"paper":"/paper/bayesian-gan","slug":"bayesian-gan","title":"Bayesian GAN","date":"2017-05-26","arxiv_id":"1705.09558","n_code_links":4,"syntology":null},{"paper":null,"slug":"enhancement-of-ssd-by-concatenating-feature","title":"Enhancement of SSD by concatenating feature maps for object detection","date":"2017-05-26","arxiv_id":"1705.09587","n_code_links":0,"syntology":null},{"paper":"/paper/continual-learning-with-deep-generative","slug":"continual-learning-with-deep-generative","title":"Continual Learning with Deep Generative Replay","date":"2017-05-24","arxiv_id":"1705.08690","n_code_links":5,"syntology":null},{"paper":"/paper/deep-voice-2-multi-speaker-neural-text-to","slug":"deep-voice-2-multi-speaker-neural-text-to","title":"Deep Voice 2: Multi-Speaker Neural Text-to-Speech","date":"2017-05-24","arxiv_id":"1705.08947","n_code_links":1,"syntology":null},{"paper":null,"slug":"diminishing-batch-normalization","title":"Diminishing Batch Normalization","date":"2017-05-22","arxiv_id":"1705.08011","n_code_links":0,"syntology":null},{"paper":"/paper/semantically-decomposing-the-latent-spaces-of","slug":"semantically-decomposing-the-latent-spaces-of","title":"Semantically Decomposing the Latent Spaces of Generative Adversarial Networks","date":"2017-05-22","arxiv_id":"1705.07904","n_code_links":1,"syntology":null},{"paper":"/paper/recurrent-scene-parsing-with-perspective","slug":"recurrent-scene-parsing-with-perspective","title":"Recurrent Scene Parsing with Perspective Understanding in the Loop","date":"2017-05-20","arxiv_id":"1705.07238","n_code_links":1,"syntology":null},{"paper":null,"slug":"discrete-sequential-prediction-of-continuous","title":"Discrete Sequential Prediction of Continuous Actions for Deep RL","date":"2017-05-14","arxiv_id":"1705.05035","n_code_links":0,"syntology":null},{"paper":"/paper/icnet-for-real-time-semantic-segmentation-on","slug":"icnet-for-real-time-semantic-segmentation-on","title":"ICNet for Real-Time Semantic Segmentation on High-Resolution Images","date":"2017-04-27","arxiv_id":"1704.08545","n_code_links":18,"syntology":null},{"paper":null,"slug":"automatic-liver-lesion-segmentation-using-a","title":"Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method","date":"2017-04-24","arxiv_id":"1704.07239","n_code_links":0,"syntology":null},{"paper":"/paper/convolutional-neural-networks-for-facial","slug":"convolutional-neural-networks-for-facial","title":"Convolutional Neural Networks for Facial Expression Recognition","date":"2017-04-22","arxiv_id":"1704.06756","n_code_links":1,"syntology":null},{"paper":"/paper/skeleton-based-action-recognition-using-1","slug":"skeleton-based-action-recognition-using-1","title":"Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn","date":"2017-04-19","arxiv_id":"1704.05645","n_code_links":0,"syntology":null},{"paper":"/paper/learning-to-reason-end-to-end-module-networks","slug":"learning-to-reason-end-to-end-module-networks","title":"Learning to Reason: End-to-End Module Networks for Visual Question Answering","date":"2017-04-18","arxiv_id":"1704.05526","n_code_links":1,"syntology":null},{"paper":"/paper/mobilenets-efficient-convolutional-neural","slug":"mobilenets-efficient-convolutional-neural","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","date":"2017-04-17","arxiv_id":"1704.04861","n_code_links":159,"syntology":{"ran":53,"of":83,"n_ran_checked":44,"n_instrument":9,"unverified":30,"pointer_only":48,"phrase":"53 ran (of which 28 constructed an object rather than computing a result; 44 with no instrument failure: 4 honoured, 0 violated, 40 with no contract checked; 9 where Syntology's instrument failed) · 30 unverified","official":{"repos":["tensorflow/tensorflow"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"paper":null,"slug":"fastventricle-cardiac-segmentation-with-enet","title":"FastVentricle: Cardiac Segmentation with ENet","date":"2017-04-13","arxiv_id":"1704.04296","n_code_links":0,"syntology":null},{"paper":"/paper/on-the-effects-of-batch-and-weight","slug":"on-the-effects-of-batch-and-weight","title":"On the Effects of Batch and Weight Normalization in Generative Adversarial Networks","date":"2017-04-13","arxiv_id":"1704.03971","n_code_links":3,"syntology":null},{"paper":null,"slug":"deep-contextual-recurrent-residual-networks","title":"Deep Contextual Recurrent Residual Networks for Scene Labeling","date":"2017-04-12","arxiv_id":"1704.03594","n_code_links":0,"syntology":null},{"paper":"/paper/cutting-the-error-by-half-investigation-of","slug":"cutting-the-error-by-half-investigation-of","title":"Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification","date":"2017-04-11","arxiv_id":"1704.03557","n_code_links":5,"syntology":null},{"paper":"/paper/show-ask-attend-and-answer-a-strong-baseline","slug":"show-ask-attend-and-answer-a-strong-baseline","title":"Show, Ask, Attend, and Answer: A Strong Baseline For Visual Question Answering","date":"2017-04-11","arxiv_id":"1704.03162","n_code_links":13,"syntology":{"ran":9,"of":9,"n_ran_checked":2,"n_instrument":7,"unverified":0,"pointer_only":9,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 7 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"automatic-liver-lesion-detection-using","title":"Automatic Liver Lesion Detection using Cascaded Deep Residual Networks","date":"2017-04-10","arxiv_id":"1704.02703","n_code_links":0,"syntology":null},{"paper":"/paper/snapshot-ensembles-train-1-get-m-for-free","slug":"snapshot-ensembles-train-1-get-m-for-free","title":"Snapshot Ensembles: Train 1, get M for free","date":"2017-04-01","arxiv_id":"1704.00109","n_code_links":11,"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":{"repos":["gaohuang/SnapshotEnsemble"],"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/improved-training-of-wasserstein-gans","slug":"improved-training-of-wasserstein-gans","title":"Improved Training of Wasserstein GANs","date":"2017-03-31","arxiv_id":"1704.00028","n_code_links":110,"syntology":{"ran":27,"of":49,"n_ran_checked":19,"n_instrument":8,"unverified":22,"pointer_only":20,"phrase":"27 ran (of which 10 constructed an object rather than computing a result; 19 with no instrument failure: 1 honoured, 0 violated, 18 with no contract checked; 8 where Syntology's instrument failed) · 22 unverified","official":{"repos":["igul222/improved_wgan_training"],"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/unpaired-image-to-image-translation-using","slug":"unpaired-image-to-image-translation-using","title":"Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks","date":"2017-03-30","arxiv_id":"1703.10593","n_code_links":190,"syntology":{"ran":14,"of":31,"n_ran_checked":9,"n_instrument":5,"unverified":17,"pointer_only":6,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 0 violated, 8 with no contract checked; 5 where Syntology's instrument failed) · 17 unverified","official":{"repos":["junyanz/CycleGAN","junyanz/pytorch-CycleGAN-and-pix2pix"],"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/tacotron-towards-end-to-end-speech-synthesis","slug":"tacotron-towards-end-to-end-speech-synthesis","title":"Tacotron: Towards End-to-End Speech Synthesis","date":"2017-03-29","arxiv_id":"1703.10135","n_code_links":30,"syntology":{"ran":16,"of":25,"n_ran_checked":13,"n_instrument":3,"unverified":9,"pointer_only":6,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 3 honoured, 1 violated, 9 with no contract checked; 3 where Syntology's instrument failed) · 9 unverified","official":null}},{"paper":"/paper/adversarial-transformation-networks-learning","slug":"adversarial-transformation-networks-learning","title":"Adversarial Transformation Networks: Learning to Generate Adversarial Examples","date":"2017-03-28","arxiv_id":"1703.09387","n_code_links":2,"syntology":null},{"paper":"/paper/active-convolution-learning-the-shape-of","slug":"active-convolution-learning-the-shape-of","title":"Active Convolution: Learning the Shape of Convolution for Image Classification","date":"2017-03-27","arxiv_id":"1703.09076","n_code_links":1,"syntology":null},{"paper":"/paper/scaling-the-scattering-transform-deep-hybrid","slug":"scaling-the-scattering-transform-deep-hybrid","title":"Scaling the Scattering Transform: Deep Hybrid Networks","date":"2017-03-27","arxiv_id":"1703.08961","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["edouardoyallon/pyscatwave","edouardoyallon/scalingscattering"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/is-second-order-information-helpful-for-large","slug":"is-second-order-information-helpful-for-large","title":"Is Second-order Information Helpful for Large-scale Visual Recognition?","date":"2017-03-23","arxiv_id":"1703.08050","n_code_links":1,"syntology":null},{"paper":"/paper/mask-r-cnn","slug":"mask-r-cnn","title":"Mask R-CNN","date":"2017-03-20","arxiv_id":"1703.06870","n_code_links":179,"syntology":{"ran":101,"of":140,"n_ran_checked":90,"n_instrument":11,"unverified":39,"pointer_only":32,"phrase":"101 ran (of which 3 constructed an object rather than computing a result; 90 with no instrument failure: 0 honoured, 0 violated, 90 with no contract checked; 11 where Syntology's instrument failed) · 39 unverified","official":null}},{"paper":"/paper/generating-multi-label-discrete-patient","slug":"generating-multi-label-discrete-patient","title":"Generating Multi-label Discrete Patient Records using Generative Adversarial Networks","date":"2017-03-19","arxiv_id":"1703.06490","n_code_links":3,"syntology":{"ran":6,"of":9,"n_ran_checked":1,"n_instrument":5,"unverified":3,"pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","official":{"repos":["mp2893/medgan"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/deformable-convolutional-networks","slug":"deformable-convolutional-networks","title":"Deformable Convolutional Networks","date":"2017-03-17","arxiv_id":"1703.06211","n_code_links":38,"syntology":{"ran":6,"of":9,"n_ran_checked":4,"n_instrument":2,"unverified":3,"pointer_only":4,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","official":{"repos":["msracver/Deformable-ConvNets"],"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/look-into-person-self-supervised-structure","slug":"look-into-person-self-supervised-structure","title":"Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing","date":"2017-03-16","arxiv_id":"1703.05446","n_code_links":1,"syntology":null},{"paper":"/paper/svdnet-for-pedestrian-retrieval","slug":"svdnet-for-pedestrian-retrieval","title":"SVDNet for Pedestrian Retrieval","date":"2017-03-16","arxiv_id":"1703.05693","n_code_links":0,"syntology":null},{"paper":"/paper/learned-optimizers-that-scale-and-generalize","slug":"learned-optimizers-that-scale-and-generalize","title":"Learned Optimizers that Scale and Generalize","date":"2017-03-14","arxiv_id":"1703.04813","n_code_links":1,"syntology":null},{"paper":"/paper/recod-titans-at-isic-challenge-2017","slug":"recod-titans-at-isic-challenge-2017","title":"RECOD Titans at ISIC Challenge 2017","date":"2017-03-14","arxiv_id":"1703.04819","n_code_links":4,"syntology":null},{"paper":"/paper/model-agnostic-meta-learning-for-fast","slug":"model-agnostic-meta-learning-for-fast","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","date":"2017-03-09","arxiv_id":"1703.03400","n_code_links":85,"syntology":{"ran":103,"of":154,"n_ran_checked":72,"n_instrument":31,"unverified":51,"pointer_only":57,"phrase":"103 ran (of which 35 constructed an object rather than computing a result; 72 with no instrument failure: 6 honoured, 1 violated, 65 with no contract checked; 31 where Syntology's instrument failed) · 51 unverified","official":{"repos":["cbfinn/maml","cbfinn/maml_rl"],"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","unlocated"]}}},{"paper":"/paper/large-kernel-matters-improve-semantic","slug":"large-kernel-matters-improve-semantic","title":"Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network","date":"2017-03-08","arxiv_id":"1703.02719","n_code_links":2,"syntology":null},{"paper":"/paper/english-conversational-telephone-speech","slug":"english-conversational-telephone-speech","title":"English Conversational Telephone Speech Recognition by Humans and Machines","date":"2017-03-06","arxiv_id":"1703.02136","n_code_links":0,"syntology":null},{"paper":"/paper/mean-teachers-are-better-role-models-weight","slug":"mean-teachers-are-better-role-models-weight","title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","date":"2017-03-06","arxiv_id":"1703.01780","n_code_links":8,"syntology":{"ran":6,"of":6,"n_ran_checked":2,"n_instrument":4,"unverified":0,"pointer_only":6,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","official":{"repos":["CuriousAI/mean-teacher"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"paper":"/paper/lr-gan-layered-recursive-generative","slug":"lr-gan-layered-recursive-generative","title":"LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation","date":"2017-03-05","arxiv_id":"1703.01560","n_code_links":1,"syntology":null},{"paper":"/paper/understanding-convolution-for-semantic","slug":"understanding-convolution-for-semantic","title":"Understanding Convolution for Semantic Segmentation","date":"2017-02-27","arxiv_id":"1702.08502","n_code_links":5,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"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":["TuSimple/TuSimple-DUC"],"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":null,"slug":"a-novel-weight-shared-multi-stage-network","title":"A Novel Weight-Shared Multi-Stage CNN for Scale Robustness","date":"2017-02-12","arxiv_id":"1702.03505","n_code_links":0,"syntology":null},{"paper":null,"slug":"development-of-javascript-based-deep-learning","title":"Development of JavaScript-based deep learning platform and application to distributed training","date":"2017-02-07","arxiv_id":"1702.01846","n_code_links":0,"syntology":null},{"paper":null,"slug":"wide-residual-inception-networks-for-real","title":"Wide-Residual-Inception Networks for Real-time Object Detection","date":"2017-02-04","arxiv_id":"1702.01243","n_code_links":0,"syntology":null},{"paper":"/paper/deep-learning-with-low-precision-by-half-wave","slug":"deep-learning-with-low-precision-by-half-wave","title":"Deep Learning with Low Precision by Half-wave Gaussian Quantization","date":"2017-02-03","arxiv_id":"1702.00953","n_code_links":1,"syntology":null},{"paper":null,"slug":"pixel-wise-ear-detection-with-convolutional","title":"Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks","date":"2017-02-01","arxiv_id":"1702.00307","n_code_links":0,"syntology":null},{"paper":null,"slug":"modularized-morphing-of-neural-networks","title":"Modularized Morphing of Neural Networks","date":"2017-01-12","arxiv_id":"1701.03281","n_code_links":0,"syntology":null},{"paper":"/paper/oriented-response-networks","slug":"oriented-response-networks","title":"Oriented Response Networks","date":"2017-01-07","arxiv_id":"1701.01833","n_code_links":1,"syntology":null}],"record_sha256":"7bd09769f3aaf5a193b4d699a55eb69ed5eeb9a37418b938f26d8ed207898643","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}