{"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/60","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":60,"pages_in_order":63,"rows_per_page":100,"rows":[5901,6000],"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/59","next":"/method/batch-normalization/papers/61","papers":[{"paper":"/paper/distributed-distributional-deterministic","slug":"distributed-distributional-deterministic","title":"Distributed Distributional Deterministic Policy Gradients","date":"2018-04-23","arxiv_id":"1804.08617","n_code_links":5,"syntology":null},{"paper":null,"slug":"study-of-residual-networks-for-image","title":"Study of Residual Networks for Image Recognition","date":"2018-04-21","arxiv_id":"1805.00325","n_code_links":0,"syntology":null},{"paper":"/paper/detnet-a-backbone-network-for-object","slug":"detnet-a-backbone-network-for-object","title":"DetNet: A Backbone network for Object Detection","date":"2018-04-17","arxiv_id":"1804.06215","n_code_links":2,"syntology":null},{"paper":"/paper/simple-baselines-for-human-pose-estimation","slug":"simple-baselines-for-human-pose-estimation","title":"Simple Baselines for Human Pose Estimation and Tracking","date":"2018-04-17","arxiv_id":"1804.06208","n_code_links":27,"syntology":null},{"paper":null,"slug":"sparsenet-a-sparse-densenet-for-image","title":"SparseNet: A Sparse DenseNet for Image Classification","date":"2018-04-15","arxiv_id":"1804.05340","n_code_links":0,"syntology":null},{"paper":null,"slug":"melanogans-high-resolution-skin-lesion","title":"MelanoGANs: High Resolution Skin Lesion Synthesis with GANs","date":"2018-04-12","arxiv_id":"1804.04338","n_code_links":0,"syntology":null},{"paper":"/paper/exfuse-enhancing-feature-fusion-for-semantic","slug":"exfuse-enhancing-feature-fusion-for-semantic","title":"ExFuse: Enhancing Feature Fusion for Semantic Segmentation","date":"2018-04-11","arxiv_id":"1804.03821","n_code_links":0,"syntology":null},{"paper":null,"slug":"loss-rank-mining-a-general-hard-example","title":"Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors","date":"2018-04-10","arxiv_id":"1804.04606","n_code_links":0,"syntology":null},{"paper":"/paper/hyperdense-net-a-hyper-densely-connected-cnn","slug":"hyperdense-net-a-hyper-densely-connected-cnn","title":"HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation","date":"2018-04-09","arxiv_id":"1804.02967","n_code_links":3,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":0,"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) · 0 unverified","official":{"repos":["josedolz/HyperDenseNet"],"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/markerless-tracking-of-user-defined-features","slug":"markerless-tracking-of-user-defined-features","title":"Markerless tracking of user-defined features with deep learning","date":"2018-04-09","arxiv_id":"1804.03142","n_code_links":1,"syntology":null},{"paper":"/paper/netadapt-platform-aware-neural-network","slug":"netadapt-platform-aware-neural-network","title":"NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications","date":"2018-04-09","arxiv_id":"1804.03230","n_code_links":4,"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, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/recovering-realistic-texture-in-image-super","slug":"recovering-realistic-texture-in-image-super","title":"Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform","date":"2018-04-09","arxiv_id":"1804.02815","n_code_links":4,"syntology":null},{"paper":null,"slug":"language-modeling-with-generative","title":"Language Modeling with Generative AdversarialNetworks","date":"2018-04-08","arxiv_id":"1804.02617","n_code_links":0,"syntology":null},{"paper":"/paper/yolov3-an-incremental-improvement","slug":"yolov3-an-incremental-improvement","title":"YOLOv3: An Incremental Improvement","date":"2018-04-08","arxiv_id":"1804.02767","n_code_links":311,"syntology":{"ran":94,"of":124,"n_ran_checked":83,"n_instrument":11,"unverified":30,"pointer_only":24,"phrase":"94 ran (of which 0 constructed an object rather than computing a result; 83 with no instrument failure: 4 honoured, 2 violated, 77 with no contract checked; 11 where Syntology's instrument failed) · 30 unverified","official":null}},{"paper":null,"slug":"scalable-sentiment-for-sequence-to-sequence","title":"Scalable Sentiment for Sequence-to-sequence Chatbot Response with Performance Analysis","date":"2018-04-07","arxiv_id":"1804.02504","n_code_links":0,"syntology":null},{"paper":"/paper/mix-and-match-networks-encoder-decoder","slug":"mix-and-match-networks-encoder-decoder","title":"Mix and match networks: encoder-decoder alignment for zero-pair image translation","date":"2018-04-06","arxiv_id":"1804.02199","n_code_links":1,"syntology":null},{"paper":null,"slug":"question-type-guided-attention-in-visual","title":"Question Type Guided Attention in Visual Question Answering","date":"2018-04-06","arxiv_id":"1804.02088","n_code_links":0,"syntology":null},{"paper":"/paper/look-into-person-joint-body-parsing-pose","slug":"look-into-person-joint-body-parsing-pose","title":"Look into Person: Joint Body Parsing & Pose Estimation Network and A New Benchmark","date":"2018-04-05","arxiv_id":"1804.01984","n_code_links":3,"syntology":null},{"paper":"/paper/resnet-sparsifier-learning-strict-identity","slug":"resnet-sparsifier-learning-strict-identity","title":"Learning Strict Identity Mappings in Deep Residual Networks","date":"2018-04-05","arxiv_id":"1804.01661","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-scale-spatially-asymmetric","title":"Multi-Scale Spatially-Asymmetric Recalibration for Image Classification","date":"2018-04-03","arxiv_id":"1804.00787","n_code_links":0,"syntology":null},{"paper":null,"slug":"high-quality-nonparallel-voice-conversion","title":"High-quality nonparallel voice conversion based on cycle-consistent adversarial network","date":"2018-04-02","arxiv_id":"1804.00425","n_code_links":0,"syntology":null},{"paper":null,"slug":"multilayer-complex-network-descriptors-for","title":"Multilayer Complex Network Descriptors for Color-Texture Characterization","date":"2018-04-02","arxiv_id":"1804.00501","n_code_links":0,"syntology":null},{"paper":"/paper/adaptive-affinity-fields-for-semantic","slug":"adaptive-affinity-fields-for-semantic","title":"Adaptive Affinity Fields for Semantic Segmentation","date":"2018-03-27","arxiv_id":"1803.10335","n_code_links":1,"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: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"incremental-training-of-deep-convolutional","title":"Incremental Training of Deep Convolutional Neural Networks","date":"2018-03-27","arxiv_id":"1803.10232","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-systematic-comparison-of-deep-learning","title":"A Systematic Comparison of Deep Learning Architectures in an Autonomous Vehicle","date":"2018-03-26","arxiv_id":"1803.09386","n_code_links":0,"syntology":null},{"paper":"/paper/light-gated-recurrent-units-for-speech","slug":"light-gated-recurrent-units-for-speech","title":"Light Gated Recurrent Units for Speech Recognition","date":"2018-03-26","arxiv_id":"1803.10225","n_code_links":1,"syntology":null},{"paper":"/paper/merging-and-evolution-improving-convolutional","slug":"merging-and-evolution-improving-convolutional","title":"Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications","date":"2018-03-24","arxiv_id":"1803.09127","n_code_links":2,"syntology":null},{"paper":"/paper/towards-end-to-end-prosody-transfer-for","slug":"towards-end-to-end-prosody-transfer-for","title":"Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron","date":"2018-03-24","arxiv_id":"1803.09047","n_code_links":3,"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":"/paper/context-encoding-for-semantic-segmentation","slug":"context-encoding-for-semantic-segmentation","title":"Context Encoding for Semantic Segmentation","date":"2018-03-23","arxiv_id":"1803.08904","n_code_links":12,"syntology":{"ran":6,"of":8,"n_ran_checked":5,"n_instrument":1,"unverified":2,"pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["zhanghang1989/PyTorch-Encoding"],"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":"iterative-low-rank-approximation-for-cnn","title":"Iterative Low-Rank Approximation for CNN Compression","date":"2018-03-23","arxiv_id":"1803.08995","n_code_links":0,"syntology":null},{"paper":"/paper/pyramid-stereo-matching-network","slug":"pyramid-stereo-matching-network","title":"Pyramid Stereo Matching Network","date":"2018-03-23","arxiv_id":"1803.08669","n_code_links":6,"syntology":{"ran":10,"of":11,"n_ran_checked":9,"n_instrument":1,"unverified":1,"pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 1 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["JiaRenChang/PSMNet"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/squeezenext-hardware-aware-neural-network","slug":"squeezenext-hardware-aware-neural-network","title":"SqueezeNext: Hardware-Aware Neural Network Design","date":"2018-03-23","arxiv_id":"1803.10615","n_code_links":8,"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":["amirgholami/SqueezeNext"],"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/style-tokens-unsupervised-style-modeling","slug":"style-tokens-unsupervised-style-modeling","title":"Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis","date":"2018-03-23","arxiv_id":"1803.09017","n_code_links":11,"syntology":{"ran":19,"of":21,"n_ran_checked":13,"n_instrument":6,"unverified":2,"pointer_only":9,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 6 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/a-quantization-friendly-separable-convolution","slug":"a-quantization-friendly-separable-convolution","title":"A Quantization-Friendly Separable Convolution for MobileNets","date":"2018-03-22","arxiv_id":"1803.08607","n_code_links":1,"syntology":null},{"paper":"/paper/densely-connected-pyramid-dehazing-network","slug":"densely-connected-pyramid-dehazing-network","title":"Densely Connected Pyramid Dehazing Network","date":"2018-03-22","arxiv_id":"1803.08396","n_code_links":1,"syntology":null},{"paper":"/paper/group-normalization","slug":"group-normalization","title":"Group Normalization","date":"2018-03-22","arxiv_id":"1803.08494","n_code_links":22,"syntology":{"ran":7,"of":15,"n_ran_checked":5,"n_instrument":2,"unverified":8,"pointer_only":5,"phrase":"7 ran (of which 2 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 8 unverified","official":{"repos":["ppwwyyxx/GroupNorm-reproduce"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"adversarial-defense-based-on-structure-to","title":"Adversarial Defense based on Structure-to-Signal Autoencoders","date":"2018-03-21","arxiv_id":"1803.07994","n_code_links":0,"syntology":null},{"paper":null,"slug":"assessing-shape-bias-property-of","title":"Assessing Shape Bias Property of Convolutional Neural Networks","date":"2018-03-21","arxiv_id":"1803.07739","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-and-recognizing-human-action-from","title":"Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks","date":"2018-03-21","arxiv_id":"1803.07780","n_code_links":0,"syntology":null},{"paper":"/paper/diagnostic-classification-of-lung-nodules","slug":"diagnostic-classification-of-lung-nodules","title":"Diagnostic Classification Of Lung Nodules Using 3D Neural Networks","date":"2018-03-19","arxiv_id":"1803.07192","n_code_links":2,"syntology":null},{"paper":"/paper/zero-shot-detection","slug":"zero-shot-detection","title":"Zero-Shot Detection","date":"2018-03-19","arxiv_id":"1803.07113","n_code_links":1,"syntology":null},{"paper":null,"slug":"cross-modality-image-synthesis-from-unpaired","title":"Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size","date":"2018-03-18","arxiv_id":"1803.06629","n_code_links":0,"syntology":null},{"paper":"/paper/seqface-make-full-use-of-sequence-information","slug":"seqface-make-full-use-of-sequence-information","title":"SeqFace: Make full use of sequence information for face recognition","date":"2018-03-17","arxiv_id":"1803.06524","n_code_links":1,"syntology":null},{"paper":"/paper/complex-yolo-real-time-3d-object-detection-on","slug":"complex-yolo-real-time-3d-object-detection-on","title":"Complex-YOLO: Real-time 3D Object Detection on Point Clouds","date":"2018-03-16","arxiv_id":"1803.06199","n_code_links":10,"syntology":null},{"paper":"/paper/dynamic-structured-semantic-propagation","slug":"dynamic-structured-semantic-propagation","title":"Dynamic-structured Semantic Propagation Network","date":"2018-03-16","arxiv_id":"1803.06067","n_code_links":0,"syntology":null},{"paper":null,"slug":"studying-invariances-of-trained-convolutional","title":"Studying Invariances of Trained Convolutional Neural Networks","date":"2018-03-15","arxiv_id":"1803.05963","n_code_links":0,"syntology":null},{"paper":"/paper/averaging-weights-leads-to-wider-optima-and","slug":"averaging-weights-leads-to-wider-optima-and","title":"Averaging Weights Leads to Wider Optima and Better Generalization","date":"2018-03-14","arxiv_id":"1803.05407","n_code_links":17,"syntology":{"ran":7,"of":9,"n_ran_checked":5,"n_instrument":2,"unverified":2,"pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 1 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["timgaripov/swa"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"learning-to-explore-with-meta-policy-gradient","title":"Learning to Explore with Meta-Policy Gradient","date":"2018-03-13","arxiv_id":"1803.05044","n_code_links":0,"syntology":null},{"paper":"/paper/heneta-highly-efficient-convolutional-neural","slug":"heneta-highly-efficient-convolutional-neural","title":"HENet:A Highly Efficient Convolutional Neural Networks Optimized for Accuracy, Speed and Storage","date":"2018-03-07","arxiv_id":"1803.02742","n_code_links":1,"syntology":null},{"paper":"/paper/rtseg-real-time-semantic-segmentation","slug":"rtseg-real-time-semantic-segmentation","title":"RTSeg: Real-time Semantic Segmentation Comparative Study","date":"2018-03-07","arxiv_id":"1803.02758","n_code_links":2,"syntology":null},{"paper":null,"slug":"wngrad-learn-the-learning-rate-in-gradient","title":"WNGrad: Learn the Learning Rate in Gradient Descent","date":"2018-03-07","arxiv_id":"1803.02865","n_code_links":0,"syntology":null},{"paper":null,"slug":"comparison-of-deep-learning-approaches-for","title":"Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification","date":"2018-03-06","arxiv_id":"1803.02315","n_code_links":0,"syntology":null},{"paper":null,"slug":"early-start-intention-detection-of-cyclists","title":"Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network","date":"2018-03-06","arxiv_id":"1803.02242","n_code_links":0,"syntology":null},{"paper":null,"slug":"abnormality-detection-in-mammography-using","title":"Abnormality Detection in Mammography using Deep Convolutional Neural Networks","date":"2018-03-05","arxiv_id":"1803.01906","n_code_links":0,"syntology":null},{"paper":"/paper/path-aggregation-network-for-instance","slug":"path-aggregation-network-for-instance","title":"Path Aggregation Network for Instance Segmentation","date":"2018-03-05","arxiv_id":"1803.01534","n_code_links":10,"syntology":{"ran":3,"of":4,"n_ran_checked":2,"n_instrument":1,"unverified":1,"pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ShuLiu1993/PANet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/distributed-prioritized-experience-replay","slug":"distributed-prioritized-experience-replay","title":"Distributed Prioritized Experience Replay","date":"2018-03-02","arxiv_id":"1803.00933","n_code_links":15,"syntology":{"ran":9,"of":15,"n_ran_checked":9,"n_instrument":0,"unverified":6,"pointer_only":3,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","official":null}},{"paper":"/paper/pose-robust-face-recognition-via-deep","slug":"pose-robust-face-recognition-via-deep","title":"Pose-Robust Face Recognition via Deep Residual Equivariant Mapping","date":"2018-03-02","arxiv_id":"1803.00839","n_code_links":1,"syntology":{"ran":13,"of":15,"n_ran_checked":12,"n_instrument":1,"unverified":2,"pointer_only":1,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","official":null}},{"paper":"/paper/joint-pixel-and-feature-level-domain","slug":"joint-pixel-and-feature-level-domain","title":"Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild","date":"2018-02-28","arxiv_id":"1803.00068","n_code_links":1,"syntology":null},{"paper":"/paper/augmented-cyclegan-learning-many-to-many","slug":"augmented-cyclegan-learning-many-to-many","title":"Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data","date":"2018-02-27","arxiv_id":"1802.10151","n_code_links":3,"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":null}},{"paper":null,"slug":"recurrent-residual-module-for-fast-inference","title":"Recurrent Residual Module for Fast Inference in Videos","date":"2018-02-27","arxiv_id":"1802.09723","n_code_links":0,"syntology":null},{"paper":"/paper/yedrouj-net-an-efficient-cnn-for-spatial","slug":"yedrouj-net-an-efficient-cnn-for-spatial","title":"Yedrouj-Net: An efficient CNN for spatial steganalysis","date":"2018-02-26","arxiv_id":"1803.00407","n_code_links":1,"syntology":null},{"paper":null,"slug":"functional-gradient-boosting-based-on","title":"Functional Gradient Boosting based on Residual Network Perception","date":"2018-02-25","arxiv_id":"1802.09031","n_code_links":0,"syntology":null},{"paper":null,"slug":"wide-compression-tensor-ring-nets","title":"Wide Compression: Tensor Ring Nets","date":"2018-02-25","arxiv_id":"1802.09052","n_code_links":0,"syntology":null},{"paper":"/paper/residual-dense-network-for-image-super","slug":"residual-dense-network-for-image-super","title":"Residual Dense Network for Image Super-Resolution","date":"2018-02-24","arxiv_id":"1802.08797","n_code_links":16,"syntology":{"ran":20,"of":24,"n_ran_checked":15,"n_instrument":5,"unverified":4,"pointer_only":4,"phrase":"20 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 2 honoured, 0 violated, 13 with no contract checked; 5 where Syntology's instrument failed) · 4 unverified","official":{"repos":["yulunzhang/RDN"],"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/bayesian-uncertainty-estimation-for-batch","slug":"bayesian-uncertainty-estimation-for-batch","title":"Bayesian Uncertainty Estimation for Batch Normalized Deep Networks","date":"2018-02-18","arxiv_id":"1802.06455","n_code_links":4,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["iclr-mcbn/mcbn","icml-mcbn/mcbn"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"paper":"/paper/towards-principled-design-of-deep","slug":"towards-principled-design-of-deep","title":"Towards Principled Design of Deep Convolutional Networks: Introducing SimpNet","date":"2018-02-17","arxiv_id":"1802.06205","n_code_links":1,"syntology":null},{"paper":"/paper/spectral-normalization-for-generative","slug":"spectral-normalization-for-generative","title":"Spectral Normalization for Generative Adversarial Networks","date":"2018-02-16","arxiv_id":"1802.05957","n_code_links":38,"syntology":{"ran":25,"of":31,"n_ran_checked":21,"n_instrument":4,"unverified":6,"pointer_only":15,"phrase":"25 ran (of which 9 constructed an object rather than computing a result; 21 with no instrument failure: 0 honoured, 1 violated, 20 with no contract checked; 4 where Syntology's instrument failed) · 6 unverified","official":{"repos":["pfnet-research/sngan_projection"],"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","named_in_paper"]}}},{"paper":"/paper/cgans-with-projection-discriminator","slug":"cgans-with-projection-discriminator","title":"cGANs with Projection Discriminator","date":"2018-02-15","arxiv_id":"1802.05637","n_code_links":12,"syntology":{"ran":9,"of":9,"n_ran_checked":4,"n_instrument":5,"unverified":0,"pointer_only":6,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 2 violated, 1 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","official":{"repos":["crcrpar/pytorch.sngan_projection","pfnet-research/sngan_projection"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/gep-pg-decoupling-exploration-and","slug":"gep-pg-decoupling-exploration-and","title":"GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms","date":"2018-02-14","arxiv_id":"1802.05054","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["flowersteam/geppg"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/training-and-inference-with-integers-in-deep","slug":"training-and-inference-with-integers-in-deep","title":"Training and Inference with Integers in Deep Neural Networks","date":"2018-02-13","arxiv_id":"1802.04680","n_code_links":3,"syntology":null},{"paper":"/paper/uncertainty-estimation-via-stochastic-batch","slug":"uncertainty-estimation-via-stochastic-batch","title":"Uncertainty Estimation via Stochastic Batch Normalization","date":"2018-02-13","arxiv_id":"1802.04893","n_code_links":1,"syntology":null},{"paper":"/paper/adversarial-audio-synthesis","slug":"adversarial-audio-synthesis","title":"Adversarial Audio Synthesis","date":"2018-02-12","arxiv_id":"1802.04208","n_code_links":22,"syntology":{"ran":2,"of":4,"n_ran_checked":0,"n_instrument":2,"unverified":2,"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) · 2 unverified","official":{"repos":["chrisdonahue/wavegan"],"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":"tempered-adversarial-networks","title":"Tempered Adversarial Networks","date":"2018-02-12","arxiv_id":"1802.04374","n_code_links":0,"syntology":null},{"paper":"/paper/fd-mobilenet-improved-mobilenet-with-a-fast","slug":"fd-mobilenet-improved-mobilenet-with-a-fast","title":"FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy","date":"2018-02-11","arxiv_id":"1802.03750","n_code_links":3,"syntology":null},{"paper":"/paper/hydra-an-ensemble-of-convolutional-neural","slug":"hydra-an-ensemble-of-convolutional-neural","title":"Hydra: an Ensemble of Convolutional Neural Networks for Geospatial Land Classification","date":"2018-02-10","arxiv_id":"1802.03518","n_code_links":1,"syntology":null},{"paper":null,"slug":"invertible-autoencoder-for-domain-adaptation","title":"Invertible Autoencoder for domain adaptation","date":"2018-02-10","arxiv_id":"1802.06869","n_code_links":0,"syntology":null},{"paper":null,"slug":"batch-kalman-normalization-towards-training","title":"Batch Kalman Normalization: Towards Training Deep Neural Networks with Micro-Batches","date":"2018-02-09","arxiv_id":"1802.03133","n_code_links":0,"syntology":null},{"paper":"/paper/encoder-decoder-with-atrous-separable","slug":"encoder-decoder-with-atrous-separable","title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","date":"2018-02-07","arxiv_id":"1802.02611","n_code_links":78,"syntology":{"ran":44,"of":72,"n_ran_checked":28,"n_instrument":16,"unverified":28,"pointer_only":40,"phrase":"44 ran (of which 17 constructed an object rather than computing a result; 28 with no instrument failure: 2 honoured, 0 violated, 26 with no contract checked; 16 where Syntology's instrument failed) · 28 unverified","official":{"repos":["tensorflow/models"],"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/shakedrop-regularization-for-deep-residual","slug":"shakedrop-regularization-for-deep-residual","title":"ShakeDrop Regularization for Deep Residual Learning","date":"2018-02-07","arxiv_id":"1802.02375","n_code_links":5,"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":{"repos":["imenurok/ShakeDrop"],"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":["unlocated"]}}},{"paper":null,"slug":"universal-deep-neural-network-compression","title":"Universal Deep Neural Network Compression","date":"2018-02-07","arxiv_id":"1802.02271","n_code_links":0,"syntology":null},{"paper":"/paper/mixed-link-networks","slug":"mixed-link-networks","title":"Mixed Link Networks","date":"2018-02-06","arxiv_id":"1802.01808","n_code_links":1,"syntology":null},{"paper":"/paper/evaluating-the-robustness-of-neural-networks","slug":"evaluating-the-robustness-of-neural-networks","title":"Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach","date":"2018-01-31","arxiv_id":"1801.10578","n_code_links":1,"syntology":null},{"paper":null,"slug":"pretraining-deep-actor-critic-reinforcement","title":"Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert Demonstrations","date":"2018-01-31","arxiv_id":"1801.10459","n_code_links":0,"syntology":null},{"paper":"/paper/deeplung-deep-3d-dual-path-nets-for-automated","slug":"deeplung-deep-3d-dual-path-nets-for-automated","title":"DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification","date":"2018-01-25","arxiv_id":"1801.09555","n_code_links":2,"syntology":null},{"paper":"/paper/nddr-cnn-layer-wise-feature-fusing-in-multi","slug":"nddr-cnn-layer-wise-feature-fusing-in-multi","title":"NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction","date":"2018-01-25","arxiv_id":"1801.08297","n_code_links":1,"syntology":null},{"paper":"/paper/numerical-coordinate-regression-with","slug":"numerical-coordinate-regression-with","title":"Numerical Coordinate Regression with Convolutional Neural Networks","date":"2018-01-23","arxiv_id":"1801.07372","n_code_links":2,"syntology":null},{"paper":"/paper/effnet-an-efficient-structure-for","slug":"effnet-an-efficient-structure-for","title":"EffNet: An Efficient Structure for Convolutional Neural Networks","date":"2018-01-19","arxiv_id":"1801.06434","n_code_links":3,"syntology":null},{"paper":"/paper/fastnet","slug":"fastnet","title":"FastNet","date":"2018-01-17","arxiv_id":"1802.02186","n_code_links":1,"syntology":null},{"paper":null,"slug":"stressednets-efficient-feature","title":"StressedNets: Efficient Feature Representations via Stress-induced Evolutionary Synthesis of Deep Neural Networks","date":"2018-01-16","arxiv_id":"1801.05387","n_code_links":0,"syntology":null},{"paper":"/paper/understanding-the-disharmony-between-dropout","slug":"understanding-the-disharmony-between-dropout","title":"Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift","date":"2018-01-16","arxiv_id":"1801.05134","n_code_links":5,"syntology":null},{"paper":null,"slug":"unsupervised-representation-learning-with","title":"Unsupervised Representation Learning with Laplacian Pyramid Auto-encoders","date":"2018-01-16","arxiv_id":"1801.05278","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-cipher-cracking-using-discrete","slug":"unsupervised-cipher-cracking-using-discrete","title":"Unsupervised Cipher Cracking Using Discrete GANs","date":"2018-01-15","arxiv_id":"1801.04883","n_code_links":1,"syntology":null},{"paper":"/paper/mobilenetv2-inverted-residuals-and-linear","slug":"mobilenetv2-inverted-residuals-and-linear","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","date":"2018-01-13","arxiv_id":"1801.04381","n_code_links":159,"syntology":{"ran":85,"of":111,"n_ran_checked":65,"n_instrument":20,"unverified":26,"pointer_only":64,"phrase":"85 ran (of which 40 constructed an object rather than computing a result; 65 with no instrument failure: 8 honoured, 0 violated, 57 with no contract checked; 20 where Syntology's instrument failed) · 26 unverified","official":null}},{"paper":null,"slug":"application-of-a-semantic-segmentation","title":"Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery","date":"2018-01-11","arxiv_id":"1801.04018","n_code_links":0,"syntology":null},{"paper":"/paper/moments-in-time-dataset-one-million-videos","slug":"moments-in-time-dataset-one-million-videos","title":"Moments in Time Dataset: one million videos for event understanding","date":"2018-01-09","arxiv_id":"1801.03150","n_code_links":4,"syntology":null},{"paper":"/paper/panoptic-segmentation","slug":"panoptic-segmentation","title":"Panoptic Segmentation","date":"2018-01-03","arxiv_id":"1801.00868","n_code_links":9,"syntology":null},{"paper":null,"slug":"a-tensor-analysis-on-dense-connectivity-via","title":"A Tensor Analysis on Dense Connectivity via Convolutional Arithmetic Circuits","date":"2018-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"enhancing-batch-normalized-convolutional","title":"Enhancing Batch Normalized Convolutional Networks using Displaced Rectifier Linear Units: A Systematic Comparative Study","date":"2018-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"gradients-explode-deep-networks-are-shallow","title":"Gradients explode - Deep Networks are shallow - ResNet explained","date":"2018-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"linearly-constrained-weights-resolving-the","title":"Linearly Constrained Weights: Resolving the Vanishing Gradient Problem by Reducing Angle Bias","date":"2018-01-01","arxiv_id":null,"n_code_links":0,"syntology":null}],"record_sha256":"4e0d5bdc4ece1c300771461d7d040d4113bccb29d663add7d499ffb6a9d23dba","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}