{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/convolution/papers/168","list_of":"/method/convolution","method":"Convolution","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":168,"pages_in_order":196,"rows_per_page":100,"rows":[16701,16800],"of":19586,"counts":{"archive_papers_tagged":19586,"with_a_code_link":8064,"where_syntology_ran_a_sample":1837,"not_listed_spam_title":0,"listed":19586,"listed_where_code_ran":1837,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1557,"every_run_a_failure_of_syntologys_instrument":280,"listed_with_a_run_with_no_instrument_failure":1557,"listed_every_run_a_failure_of_syntologys_instrument":280,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/convolution","prev":"/method/convolution/papers/167","next":"/method/convolution/papers/169","papers":[{"paper":"/paper/reversible-gans-for-memory-efficient-image-to","slug":"reversible-gans-for-memory-efficient-image-to","title":"Reversible GANs for Memory-efficient Image-to-Image Translation","date":"2019-02-07","arxiv_id":"1902.02729","n_code_links":3,"syntology":null},{"paper":null,"slug":"understanding-chat-messages-for-sticker","title":"Understanding Chat Messages for Sticker Recommendation in Messaging Apps","date":"2019-02-07","arxiv_id":"1902.02704","n_code_links":0,"syntology":null},{"paper":null,"slug":"progressive-generative-adversarial-networks","title":"Progressive Generative Adversarial Networks for Medical Image Super resolution","date":"2019-02-06","arxiv_id":"1902.02144","n_code_links":0,"syntology":null},{"paper":null,"slug":"alphastar-an-evolutionary-computation","title":"AlphaStar: An Evolutionary Computation Perspective","date":"2019-02-05","arxiv_id":"1902.01724","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-convolutional-generative-adversarial-1","title":"Deep Convolutional Generative Adversarial Networks Based Flame Detection in Video","date":"2019-02-05","arxiv_id":"1902.01824","n_code_links":0,"syntology":null},{"paper":"/paper/dvolver-efficient-pareto-optimal-neural","slug":"dvolver-efficient-pareto-optimal-neural","title":"DVOLVER: Efficient Pareto-Optimal Neural Network Architecture Search","date":"2019-02-05","arxiv_id":"1902.01654","n_code_links":1,"syntology":null},{"paper":"/paper/perturbative-gan-gan-with-perturbation-layers","slug":"perturbative-gan-gan-with-perturbation-layers","title":"Perturbative GAN: GAN with Perturbation Layers","date":"2019-02-05","arxiv_id":"1902.01514","n_code_links":2,"syntology":null},{"paper":"/paper/dual-path-multi-scale-fusion-networks-with","slug":"dual-path-multi-scale-fusion-networks-with","title":"Dual Path Multi-Scale Fusion Networks with Attention for Crowd Counting","date":"2019-02-04","arxiv_id":"1902.01115","n_code_links":2,"syntology":null},{"paper":null,"slug":"end-to-end-feature-fusion-siamese-network-for","title":"End-to-end feature fusion siamese network for adaptive visual tracking","date":"2019-02-04","arxiv_id":"1902.01057","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-single-image-fog-removal-using","title":"End-to-End Single Image Fog Removal using Enhanced Cycle Consistent Adversarial Networks","date":"2019-02-04","arxiv_id":"1902.01374","n_code_links":0,"syntology":null},{"paper":null,"slug":"optimally-scheduling-cnn-convolutions-for","title":"Optimally Scheduling CNN Convolutions for Efficient Memory Access","date":"2019-02-04","arxiv_id":"1902.01492","n_code_links":0,"syntology":null},{"paper":null,"slug":"real-time-steganalysis-for-stream-media-based","title":"Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows","date":"2019-02-04","arxiv_id":"1902.01286","n_code_links":0,"syntology":null},{"paper":"/paper/saliency-tubes-visual-explanations-for-spatio","slug":"saliency-tubes-visual-explanations-for-spatio","title":"Saliency Tubes: Visual Explanations for Spatio-Temporal Convolutions","date":"2019-02-04","arxiv_id":"1902.01078","n_code_links":1,"syntology":{"ran":1,"of":2,"n_ran_checked":1,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["alexandrosstergiou/Saliency-Tubes-Visual-Explanations-for-Spatio-Temporal-Convolutions"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/towards-pedestrian-detection-using-retinanet","slug":"towards-pedestrian-detection-using-retinanet","title":"Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge","date":"2019-02-04","arxiv_id":"1902.01031","n_code_links":1,"syntology":null},{"paper":null,"slug":"tracknet-simultaneous-object-detection-and","title":"TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis","date":"2019-02-04","arxiv_id":"1902.01466","n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-networks-and-autoencoders-the","title":"Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds","date":"2019-02-03","arxiv_id":"1902.00985","n_code_links":0,"syntology":null},{"paper":null,"slug":"micik-mining-cross-layer-inherent-similarity","title":"MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression","date":"2019-02-03","arxiv_id":"1902.00918","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-spatial-temporal-decomposition-based-deep","title":"A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting","date":"2019-02-02","arxiv_id":"1902.00636","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-lesion-boundary-segmentation-in","title":"Automatic Lesion Boundary Segmentation in Dermoscopic Images with Ensemble Deep Learning Methods","date":"2019-02-02","arxiv_id":"1902.00809","n_code_links":0,"syntology":null},{"paper":"/paper/collaborative-gan-sampling","slug":"collaborative-gan-sampling","title":"Collaborative Sampling in Generative Adversarial Networks","date":"2019-02-02","arxiv_id":"1902.00813","n_code_links":1,"syntology":null},{"paper":null,"slug":"domain-invariant-hierarchical-embedding-for","title":"Domain invariant hierarchical embedding for grocery products recognition","date":"2019-02-02","arxiv_id":"1902.00760","n_code_links":0,"syntology":null},{"paper":"/paper/online-multi-object-tracking-with-dual","slug":"online-multi-object-tracking-with-dual","title":"Online Multi-Object Tracking with Dual Matching Attention Networks","date":"2019-02-02","arxiv_id":"1902.00749","n_code_links":1,"syntology":null},{"paper":"/paper/colornet-investigating-the-importance-of","slug":"colornet-investigating-the-importance-of","title":"ColorNet: Investigating the importance of color spaces for image classification","date":"2019-02-01","arxiv_id":"1902.00267","n_code_links":1,"syntology":null},{"paper":null,"slug":"comparison-of-patch-based-conditional","title":"Comparison of Patch-Based Conditional Generative Adversarial Neural Net Models with Emphasis on Model Robustness for Use in Head and Neck Cases for MR-Only planning","date":"2019-02-01","arxiv_id":"1902.00536","n_code_links":0,"syntology":null},{"paper":null,"slug":"compressing-gans-using-knowledge-distillation","title":"Compressing GANs using Knowledge Distillation","date":"2019-02-01","arxiv_id":"1902.00159","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-hybrid-network-architectures-for","title":"Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge","date":"2019-02-01","arxiv_id":"1902.00460","n_code_links":0,"syntology":null},{"paper":"/paper/fast-and-optimal-laplacian-solver-for","slug":"fast-and-optimal-laplacian-solver-for","title":"Fast and Optimal Laplacian Solver for Gradient-Domain Image Editing using Green Function Convolution","date":"2019-02-01","arxiv_id":"1902.00176","n_code_links":1,"syntology":null},{"paper":"/paper/learnable-embedding-space-for-efficient","slug":"learnable-embedding-space-for-efficient","title":"Learnable Embedding Space for Efficient Neural Architecture Compression","date":"2019-02-01","arxiv_id":"1902.00383","n_code_links":2,"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":["Friedrich1006/ESNAC"],"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":"projection-based-25d-u-net-architecture-for","title":"Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation","date":"2019-02-01","arxiv_id":"1902.00347","n_code_links":0,"syntology":null},{"paper":null,"slug":"minimizing-negative-transfer-of-knowledge-in","title":"Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach","date":"2019-01-31","arxiv_id":"1901.11512","n_code_links":0,"syntology":null},{"paper":"/paper/f-anogan-fast-unsupervised-anomaly-detection","slug":"f-anogan-fast-unsupervised-anomaly-detection","title":"f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks","date":"2019-01-30","arxiv_id":null,"n_code_links":3,"syntology":null},{"paper":null,"slug":"robust-x-ray-sparse-view-phase-tomography-via","title":"Robust X-ray Sparse-view Phase Tomography via Hierarchical Synthesis Convolutional Neural Networks","date":"2019-01-30","arxiv_id":"1901.10644","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-push-pull-layer-improves-robustness-of","title":"A Push-Pull Layer Improves Robustness of Convolutional Neural Networks","date":"2019-01-29","arxiv_id":"1901.10208","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-driven-tree-structured","title":"Attention-driven Tree-structured Convolutional LSTM for High Dimensional Data Understanding","date":"2019-01-29","arxiv_id":"1902.10053","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-adversarial-networks-for-geometric","title":"Generative Adversarial Networks for geometric surfaces prediction in injection molding","date":"2019-01-29","arxiv_id":"1901.10178","n_code_links":0,"syntology":null},{"paper":"/paper/mask-rcnn-and-u-net-ensembled-for-nuclei","slug":"mask-rcnn-and-u-net-ensembled-for-nuclei","title":"Mask-RCNN and U-net Ensembled for Nuclei Segmentation","date":"2019-01-29","arxiv_id":"1901.10170","n_code_links":1,"syntology":null},{"paper":"/paper/pa-gan-improving-gan-training-by-progressive","slug":"pa-gan-improving-gan-training-by-progressive","title":"Progressive Augmentation of GANs","date":"2019-01-29","arxiv_id":"1901.10422","n_code_links":1,"syntology":null},{"paper":"/paper/pay-less-attention-with-lightweight-and","slug":"pay-less-attention-with-lightweight-and","title":"Pay Less Attention with Lightweight and Dynamic Convolutions","date":"2019-01-29","arxiv_id":"1901.10430","n_code_links":3,"syntology":null},{"paper":"/paper/real-time-hand-gesture-detection-and","slug":"real-time-hand-gesture-detection-and","title":"Real-time Hand Gesture Detection and Classification Using Convolutional Neural Networks","date":"2019-01-29","arxiv_id":"1901.10323","n_code_links":5,"syntology":null},{"paper":null,"slug":"visualizing-and-understanding-generative","title":"On the Units of GANs (Extended Abstract)","date":"2019-01-29","arxiv_id":"1901.09887","n_code_links":0,"syntology":null},{"paper":null,"slug":"coconet-a-collaborative-convolutional-network","title":"CoCoNet: A Collaborative Convolutional Network","date":"2019-01-28","arxiv_id":"1901.09886","n_code_links":0,"syntology":null},{"paper":"/paper/convolutional-neural-networks-with-layer","slug":"convolutional-neural-networks-with-layer","title":"Convolutional Neural Networks with Layer Reuse","date":"2019-01-28","arxiv_id":"1901.09615","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-to-clean-a-gan-perspective","title":"Learning to Clean: A GAN Perspective","date":"2019-01-28","arxiv_id":"1901.11382","n_code_links":0,"syntology":null},{"paper":"/paper/out-of-sample-testing-for-gans","slug":"out-of-sample-testing-for-gans","title":"Out-of-Sample Testing for GANs","date":"2019-01-28","arxiv_id":"1901.09557","n_code_links":3,"syntology":null},{"paper":null,"slug":"deconstructing-generative-adversarial","title":"Deconstructing Generative Adversarial Networks","date":"2019-01-27","arxiv_id":"1901.09465","n_code_links":0,"syntology":null},{"paper":null,"slug":"reward-shaping-via-meta-learning","title":"Reward Shaping via Meta-Learning","date":"2019-01-27","arxiv_id":"1901.09330","n_code_links":0,"syntology":null},{"paper":null,"slug":"tunet-incorporating-segmentation-maps-to","title":"TUNet: Incorporating segmentation maps to improve classification","date":"2019-01-27","arxiv_id":"1901.11379","n_code_links":0,"syntology":null},{"paper":"/paper/atrous-convolutional-neural-network-acnn-for","slug":"atrous-convolutional-neural-network-acnn-for","title":"ACNN: a Full Resolution DCNN for Medical Image Segmentation","date":"2019-01-26","arxiv_id":"1901.09203","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-convolutional-encoder-decoders-with","title":"Deep Convolutional Encoder-Decoders with Aggregated Multi-Resolution Skip Connections for Skin Lesion Segmentation","date":"2019-01-26","arxiv_id":"1901.09197","n_code_links":0,"syntology":null},{"paper":"/paper/deepsz-a-novel-framework-to-compress-deep","slug":"deepsz-a-novel-framework-to-compress-deep","title":"DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression","date":"2019-01-26","arxiv_id":"1901.09124","n_code_links":1,"syntology":null},{"paper":"/paper/gcn-gan-a-non-linear-temporal-link-prediction","slug":"gcn-gan-a-non-linear-temporal-link-prediction","title":"GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks","date":"2019-01-26","arxiv_id":"1901.09165","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":"/paper/progressive-image-deraining-networks-a-better","slug":"progressive-image-deraining-networks-a-better","title":"Progressive Image Deraining Networks: A Better and Simpler Baseline","date":"2019-01-26","arxiv_id":"1901.09221","n_code_links":4,"syntology":{"ran":9,"of":9,"n_ran_checked":9,"n_instrument":0,"unverified":0,"pointer_only":1,"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) · 0 unverified","official":{"repos":["csdwren/PReNet"],"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":"witnessing-adversarial-training-in","title":"Kernel-Guided Training of Implicit Generative Models with Stability Guarantees","date":"2019-01-26","arxiv_id":"1901.09206","n_code_links":0,"syntology":null},{"paper":null,"slug":"diversity-sensitive-conditional-generative","title":"Diversity-Sensitive Conditional Generative Adversarial Networks","date":"2019-01-25","arxiv_id":"1901.09024","n_code_links":0,"syntology":null},{"paper":"/paper/equivariant-transformer-networks","slug":"equivariant-transformer-networks","title":"Equivariant Transformer Networks","date":"2019-01-25","arxiv_id":"1901.11399","n_code_links":3,"syntology":{"ran":4,"of":5,"n_ran_checked":4,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["stanford-futuredata/equivariant-transformers"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"joint-shape-learning-and-segmentation-for","title":"Joint shape learning and segmentation for medical images using a minimalistic deep network","date":"2019-01-25","arxiv_id":"1901.08824","n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-self-supervised-visual","slug":"revisiting-self-supervised-visual","title":"Revisiting Self-Supervised Visual Representation Learning","date":"2019-01-25","arxiv_id":"1901.09005","n_code_links":6,"syntology":{"ran":9,"of":15,"n_ran_checked":9,"n_instrument":0,"unverified":6,"pointer_only":7,"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":{"repos":["google/revisiting-self-supervised"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/virtual-conditional-generative-adversarial","slug":"virtual-conditional-generative-adversarial","title":"Virtual Conditional Generative Adversarial Networks","date":"2019-01-25","arxiv_id":"1901.09822","n_code_links":1,"syntology":null},{"paper":"/paper/vision-based-inspection-system-employing","slug":"vision-based-inspection-system-employing","title":"Vision-based inspection system employing computer vision & neural networks for detection of fractures in manufactured components","date":"2019-01-25","arxiv_id":"1901.08864","n_code_links":1,"syntology":null},{"paper":null,"slug":"autoshufflenet-learning-permutation-matrices","title":"AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks","date":"2019-01-24","arxiv_id":"1901.08624","n_code_links":0,"syntology":null},{"paper":"/paper/combinational-q-learning-for-dou-di-zhu","slug":"combinational-q-learning-for-dou-di-zhu","title":"Combinational Q-Learning for Dou Di Zhu","date":"2019-01-24","arxiv_id":"1901.08925","n_code_links":1,"syntology":null},{"paper":"/paper/deep-generative-learning-via-variational","slug":"deep-generative-learning-via-variational","title":"Deep Generative Learning via Variational Gradient Flow","date":"2019-01-24","arxiv_id":"1901.08469","n_code_links":2,"syntology":null},{"paper":null,"slug":"generating-and-aligning-from-data-geometries","title":"Generating and Aligning from Data Geometries with Generative Adversarial Networks","date":"2019-01-24","arxiv_id":"1901.08177","n_code_links":0,"syntology":null},{"paper":"/paper/in-defense-of-the-triplet-loss-for-visual","slug":"in-defense-of-the-triplet-loss-for-visual","title":"Boosting Standard Classification Architectures Through a Ranking Regularizer","date":"2019-01-24","arxiv_id":"1901.08616","n_code_links":1,"syntology":null},{"paper":"/paper/maximum-entropy-generators-for-energy-based","slug":"maximum-entropy-generators-for-energy-based","title":"Maximum Entropy Generators for Energy-Based Models","date":"2019-01-24","arxiv_id":"1901.08508","n_code_links":2,"syntology":null},{"paper":null,"slug":"qgan-quantized-generative-adversarial","title":"QGAN: Quantized Generative Adversarial Networks","date":"2019-01-24","arxiv_id":"1901.08263","n_code_links":0,"syntology":null},{"paper":"/paper/sequential-skip-prediction-with-few-shot-in","slug":"sequential-skip-prediction-with-few-shot-in","title":"Sequential Skip Prediction with Few-shot in Streamed Music Contents","date":"2019-01-24","arxiv_id":"1901.08203","n_code_links":1,"syntology":null},{"paper":"/paper/traditional-and-heavy-tailed-self","slug":"traditional-and-heavy-tailed-self","title":"Traditional and Heavy-Tailed Self Regularization in Neural Network Models","date":"2019-01-24","arxiv_id":"1901.08276","n_code_links":2,"syntology":{"ran":3,"of":7,"n_ran_checked":3,"n_instrument":0,"unverified":4,"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) · 4 unverified","official":null}},{"paper":"/paper/unsupervised-image-to-image-translation-with-1","slug":"unsupervised-image-to-image-translation-with-1","title":"Unsupervised Image-to-Image Translation with Self-Attention Networks","date":"2019-01-24","arxiv_id":"1901.08242","n_code_links":1,"syntology":null},{"paper":null,"slug":"using-cyclegans-for-effectively-reducing","title":"Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation","date":"2019-01-24","arxiv_id":"1901.08379","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-deep-convolutional-neural-network-for-1","title":"A deep Convolutional Neural Network for topology optimization with strong generalization ability","date":"2019-01-23","arxiv_id":"1901.07761","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-high-efficiency-fully-convolutional","title":"a high efficiency fully convolutional networks for pixel wise surface defect detection","date":"2019-01-23","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"alteregonets-a-way-to-human-augmentation","title":"AlteregoNets: a way to human augmentation","date":"2019-01-23","arxiv_id":"1901.09786","n_code_links":0,"syntology":null},{"paper":"/paper/bottom-up-object-detection-by-grouping","slug":"bottom-up-object-detection-by-grouping","title":"Bottom-up Object Detection by Grouping Extreme and Center Points","date":"2019-01-23","arxiv_id":"1901.08043","n_code_links":2,"syntology":{"ran":1,"of":5,"n_ran_checked":1,"n_instrument":0,"unverified":4,"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) · 4 unverified","official":{"repos":["xingyizhou/ExtremeNet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/deepfashion2-a-versatile-benchmark-for","slug":"deepfashion2-a-versatile-benchmark-for","title":"DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images","date":"2019-01-23","arxiv_id":"1901.07973","n_code_links":5,"syntology":{"ran":3,"of":3,"n_ran_checked":1,"n_instrument":2,"unverified":0,"pointer_only":2,"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":["switchablenorms/DeepFashion2"],"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":"distillation-strategies-for-proximal-policy","title":"Distillation Strategies for Proximal Policy Optimization","date":"2019-01-23","arxiv_id":"1901.08128","n_code_links":0,"syntology":null},{"paper":"/paper/hypergraph-convolution-and-hypergraph","slug":"hypergraph-convolution-and-hypergraph","title":"Hypergraph Convolution and Hypergraph Attention","date":"2019-01-23","arxiv_id":"1901.08150","n_code_links":1,"syntology":null},{"paper":"/paper/spatial-temporal-attention-res-tcn-for","slug":"spatial-temporal-attention-res-tcn-for","title":"Spatial-Temporal Attention Res-TCN for Skeleton-Based Dynamic Hand Gesture Recognition","date":"2019-01-23","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"toward-joint-image-generation-and-compression","title":"Toward Joint Image Generation and Compression using Generative Adversarial Networks","date":"2019-01-23","arxiv_id":"1901.07838","n_code_links":0,"syntology":null},{"paper":"/paper/towards-compact-convnets-via-structure","slug":"towards-compact-convnets-via-structure","title":"Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning","date":"2019-01-23","arxiv_id":"1901.07827","n_code_links":1,"syntology":null},{"paper":null,"slug":"tree-recognition-app-of-mount-tai-based-on","title":"Tree Recognition APP of Mount Tai Based on CNN","date":"2019-01-23","arxiv_id":"1901.11388","n_code_links":0,"syntology":null},{"paper":"/paper/u2-net-a-bayesian-u-net-model-with-epistemic","slug":"u2-net-a-bayesian-u-net-model-with-epistemic","title":"U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans","date":"2019-01-23","arxiv_id":"1901.07929","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-new-cgan-technique-for-constrained-topology","title":"A New CGAN Technique for Constrained Topology Design Optimization","date":"2019-01-22","arxiv_id":"1901.07675","n_code_links":0,"syntology":null},{"paper":"/paper/fast-accurate-and-lightweight-super","slug":"fast-accurate-and-lightweight-super","title":"Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search","date":"2019-01-22","arxiv_id":"1901.07261","n_code_links":2,"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: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["falsr/FALSR"],"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":"generation-high-resolution-3d-model-from","title":"Generation High resolution 3D model from natural language by Generative Adversarial Network","date":"2019-01-22","arxiv_id":"1901.07165","n_code_links":0,"syntology":null},{"paper":"/paper/hybrid-task-cascade-for-instance-segmentation","slug":"hybrid-task-cascade-for-instance-segmentation","title":"Hybrid Task Cascade for Instance Segmentation","date":"2019-01-22","arxiv_id":"1901.07518","n_code_links":5,"syntology":{"ran":11,"of":11,"n_ran_checked":11,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["open-mmlab/mmdetection"],"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":"understanding-geometry-of-encoder-decoder","title":"Understanding Geometry of Encoder-Decoder CNNs","date":"2019-01-22","arxiv_id":"1901.07647","n_code_links":0,"syntology":null},{"paper":"/paper/understanding-multi-step-deep-reinforcement","slug":"understanding-multi-step-deep-reinforcement","title":"Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target","date":"2019-01-22","arxiv_id":"1901.07510","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-universal-logic-operator-for-interpretable","title":"A Universal Logic Operator for Interpretable Deep Convolution Networks","date":"2019-01-20","arxiv_id":"1901.08551","n_code_links":0,"syntology":null},{"paper":"/paper/attention-based-spatial-temporal-graph","slug":"attention-based-spatial-temporal-graph","title":"Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting","date":"2019-01-20","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/hierarchical-attentional-hybrid-neural","slug":"hierarchical-attentional-hybrid-neural","title":"Hierarchical Attentional Hybrid Neural Networks for Document Classification","date":"2019-01-20","arxiv_id":"1901.06610","n_code_links":2,"syntology":null},{"paper":"/paper/spatiotemporal-multi-graph-convolution","slug":"spatiotemporal-multi-graph-convolution","title":"Spatiotemporal Multi-Graph Convolution Networkfor Ride-hailing Demand Forecasting","date":"2019-01-20","arxiv_id":null,"n_code_links":3,"syntology":null},{"paper":null,"slug":"understanding-the-importance-of-single-1","title":"Understanding the Importance of Single Directions via Representative Substitution","date":"2019-01-20","arxiv_id":"1911.05586","n_code_links":0,"syntology":null},{"paper":null,"slug":"accumulation-bit-width-scaling-for-ultra-low","title":"Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks","date":"2019-01-19","arxiv_id":"1901.06588","n_code_links":0,"syntology":null},{"paper":null,"slug":"consistent-optimization-for-single-shot","title":"Consistent Optimization for Single-Shot Object Detection","date":"2019-01-19","arxiv_id":"1901.06563","n_code_links":0,"syntology":null},{"paper":null,"slug":"convolution-forgetting-curve-model-for","title":"Convolution Forgetting Curve Model for Repeated Learning","date":"2019-01-19","arxiv_id":"1901.08114","n_code_links":0,"syntology":null},{"paper":null,"slug":"synthesizing-facial-photometries-and","title":"Synthesizing facial photometries and corresponding geometries using generative adversarial networks","date":"2019-01-19","arxiv_id":"1901.06551","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-adversarial-classifier-for","title":"Generative Adversarial Classifier for Handwriting Characters Super-Resolution","date":"2019-01-18","arxiv_id":"1901.06199","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-mutually-local-global-u-nets-for","title":"Learning Mutually Local-global U-nets For High-resolution Retinal Lesion Segmentation in Fundus Images","date":"2019-01-18","arxiv_id":"1901.06047","n_code_links":0,"syntology":null},{"paper":"/paper/on-policy-trust-region-policy-optimisation","slug":"on-policy-trust-region-policy-optimisation","title":"On-Policy Trust Region Policy Optimisation with Replay Buffers","date":"2019-01-18","arxiv_id":"1901.06212","n_code_links":2,"syntology":null}],"record_sha256":"73f4b71e8544cb6463cc08b465ffa666c00413e3d0ae50c4c8ab0973e8e97e9d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}