{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/relu/papers/60","list_of":"/method/relu","method":"ReLU","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":60,"pages_in_order":104,"rows_per_page":100,"rows":[5901,6000],"of":10350,"counts":{"archive_papers_tagged":10350,"with_a_code_link":4256,"where_syntology_ran_a_sample":1079,"not_listed_spam_title":0,"listed":10350,"listed_where_code_ran":1079,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":909,"every_run_a_failure_of_syntologys_instrument":170,"listed_with_a_run_with_no_instrument_failure":909,"listed_every_run_a_failure_of_syntologys_instrument":170,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/relu","prev":"/method/relu/papers/59","next":"/method/relu/papers/61","papers":[{"paper":null,"slug":"dense-cnn-with-self-attention-for-time-domain","title":"Dense CNN with Self-Attention for Time-Domain Speech Enhancement","date":"2020-09-03","arxiv_id":"2009.01941","n_code_links":0,"syntology":null},{"paper":"/paper/hifisinger-towards-high-fidelity-neural","slug":"hifisinger-towards-high-fidelity-neural","title":"HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis","date":"2020-09-03","arxiv_id":"2009.01776","n_code_links":1,"syntology":{"ran":7,"of":10,"n_ran_checked":7,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":null}},{"paper":null,"slug":"multi-attention-network-for-semantic","title":"Multi-Attention-Network for Semantic Segmentation of Fine Resolution Remote Sensing Images","date":"2020-09-03","arxiv_id":"2009.02130","n_code_links":0,"syntology":null},{"paper":null,"slug":"penalty-and-augmented-lagrangian-methods-for","title":"A Practical Layer-Parallel Training Algorithm for Residual Networks","date":"2020-09-03","arxiv_id":"2009.01462","n_code_links":0,"syntology":null},{"paper":"/paper/sample-efficient-automated-deep-reinforcement","slug":"sample-efficient-automated-deep-reinforcement","title":"Sample-Efficient Automated Deep Reinforcement Learning","date":"2020-09-03","arxiv_id":"2009.01555","n_code_links":1,"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: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["automl/SEARL"],"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":"/paper/the-little-w-net-that-could-state-of-the-art","slug":"the-little-w-net-that-could-state-of-the-art","title":"The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models","date":"2020-09-03","arxiv_id":"2009.01907","n_code_links":2,"syntology":null},{"paper":"/paper/deep-generative-model-for-image-inpainting","slug":"deep-generative-model-for-image-inpainting","title":"Deep Generative Model for Image Inpainting with Local Binary Pattern Learning and Spatial Attention","date":"2020-09-02","arxiv_id":"2009.01031","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-to-detect-bacterial-colonies","title":"Deep Learning to Detect Bacterial Colonies for the Production of Vaccines","date":"2020-09-02","arxiv_id":"2009.00926","n_code_links":0,"syntology":null},{"paper":"/paper/evaluation-of-deep-convolutional-generative","slug":"evaluation-of-deep-convolutional-generative","title":"Evaluation of Deep Convolutional Generative Adversarial Networks for data augmentation of chest X-ray images","date":"2020-09-02","arxiv_id":"2009.01181","n_code_links":0,"syntology":null},{"paper":"/paper/neural-crossbreed-neural-based-image","slug":"neural-crossbreed-neural-based-image","title":"Neural Crossbreed: Neural Based Image Metamorphosis","date":"2020-09-02","arxiv_id":"2009.00905","n_code_links":1,"syntology":null},{"paper":null,"slug":"speaker-representation-learning-using-global","title":"Speaker Representation Learning using Global Context Guided Channel and Time-Frequency Transformations","date":"2020-09-02","arxiv_id":"2009.00768","n_code_links":0,"syntology":null},{"paper":null,"slug":"transform-quantization-for-cnn-compression","title":"Transform Quantization for CNN (Convolutional Neural Network) Compression","date":"2020-09-02","arxiv_id":"2009.01174","n_code_links":0,"syntology":null},{"paper":"/paper/wavegrad-estimating-gradients-for-waveform","slug":"wavegrad-estimating-gradients-for-waveform","title":"WaveGrad: Estimating Gradients for Waveform Generation","date":"2020-09-02","arxiv_id":"2009.00713","n_code_links":7,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":1,"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) · 0 unverified","official":null}},{"paper":"/paper/rangercnn-towards-fast-and-accurate-3d-object","slug":"rangercnn-towards-fast-and-accurate-3d-object","title":"RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation","date":"2020-09-01","arxiv_id":"2009.00206","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/a-framework-for-contrastive-self-supervised","slug":"a-framework-for-contrastive-self-supervised","title":"A Framework For Contrastive Self-Supervised Learning And Designing A New Approach","date":"2020-08-31","arxiv_id":"2009.00104","n_code_links":2,"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":null}},{"paper":"/paper/deep-probabilistic-feature-metric-tracking","slug":"deep-probabilistic-feature-metric-tracking","title":"Deep Probabilistic Feature-metric Tracking","date":"2020-08-31","arxiv_id":"2008.13504","n_code_links":1,"syntology":null},{"paper":"/paper/extreme-memorization-via-scale-of","slug":"extreme-memorization-via-scale-of","title":"Extreme Memorization via Scale of Initialization","date":"2020-08-31","arxiv_id":"2008.13363","n_code_links":1,"syntology":null},{"paper":"/paper/langevin-cooling-for-domain-translation","slug":"langevin-cooling-for-domain-translation","title":"Langevin Cooling for Domain Translation","date":"2020-08-31","arxiv_id":"2008.13723","n_code_links":1,"syntology":null},{"paper":"/paper/self-supervised-video-representation-learning-5","slug":"self-supervised-video-representation-learning-5","title":"Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics","date":"2020-08-31","arxiv_id":"2008.13426","n_code_links":2,"syntology":{"ran":5,"of":5,"n_ran_checked":5,"n_instrument":0,"unverified":0,"pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 3 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["laura-wang/video_repres_sts"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"a-compact-deep-architecture-for-real-time","title":"A Compact Deep Architecture for Real-time Saliency Prediction","date":"2020-08-30","arxiv_id":"2008.13227","n_code_links":0,"syntology":null},{"paper":"/paper/deep-hypergraph-u-net-for-brain-graph","slug":"deep-hypergraph-u-net-for-brain-graph","title":"Deep Hypergraph U-Net for Brain Graph Embedding and Classification","date":"2020-08-30","arxiv_id":"2008.13118","n_code_links":1,"syntology":null},{"paper":"/paper/akhcrnet-bengali-handwritten-character","slug":"akhcrnet-bengali-handwritten-character","title":"AKHCRNet: Bengali Handwritten Character Recognition Using Deep Learning","date":"2020-08-29","arxiv_id":"2008.12995","n_code_links":1,"syntology":null},{"paper":"/paper/dual-attention-gans-for-semantic-image","slug":"dual-attention-gans-for-semantic-image","title":"Dual Attention GANs for Semantic Image Synthesis","date":"2020-08-29","arxiv_id":"2008.13024","n_code_links":1,"syntology":null},{"paper":"/paper/puzzle-ae-novelty-detection-in-images-through","slug":"puzzle-ae-novelty-detection-in-images-through","title":"Puzzle-AE: Novelty Detection in Images through Solving Puzzles","date":"2020-08-29","arxiv_id":"2008.12959","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-based-spectral-ct-imaging","title":"Deep Learning based Spectral CT Imaging","date":"2020-08-28","arxiv_id":"2008.13570","n_code_links":0,"syntology":null},{"paper":null,"slug":"predicting-training-time-without-training","title":"Predicting Training Time Without Training","date":"2020-08-28","arxiv_id":"2008.12478","n_code_links":0,"syntology":null},{"paper":"/paper/a-free-web-service-for-fast-covid-19","slug":"a-free-web-service-for-fast-covid-19","title":"A free web service for fast COVID-19 classification of chest X-Ray images","date":"2020-08-27","arxiv_id":"2009.01657","n_code_links":1,"syntology":null},{"paper":"/paper/improving-the-segmentation-of-scanning-probe","slug":"improving-the-segmentation-of-scanning-probe","title":"Improving the Segmentation of Scanning Probe Microscope Images using Convolutional Neural Networks","date":"2020-08-27","arxiv_id":"2008.12371","n_code_links":1,"syntology":null},{"paper":null,"slug":"w-net-dense-semantic-segmentation-of","title":"W-Net: Dense Semantic Segmentation of Subcutaneous Tissue in Ultrasound Images by Expanding U-Net to Incorporate Ultrasound RF Waveform Data","date":"2020-08-27","arxiv_id":"2008.12413","n_code_links":0,"syntology":null},{"paper":"/paper/better-than-reference-in-low-light-image","slug":"better-than-reference-in-low-light-image","title":"Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement Networks","date":"2020-08-26","arxiv_id":"2008.11434","n_code_links":1,"syntology":null},{"paper":"/paper/convolutional-neural-networks-for-detecting","slug":"convolutional-neural-networks-for-detecting","title":"Convolutional neural networks for detecting challenging cases in cloud masking using Sentinel-2 imagery","date":"2020-08-26","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/domain-adversarial-learning-for-multi-centre","slug":"domain-adversarial-learning-for-multi-centre","title":"Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation","date":"2020-08-26","arxiv_id":"2008.11776","n_code_links":1,"syntology":null},{"paper":"/paper/nas-dip-learning-deep-image-prior-with-neural","slug":"nas-dip-learning-deep-image-prior-with-neural","title":"NAS-DIP: Learning Deep Image Prior with Neural Architecture Search","date":"2020-08-26","arxiv_id":"2008.11713","n_code_links":1,"syntology":null},{"paper":null,"slug":"vehicle-trajectory-prediction-in-crowded","title":"Vehicle Trajectory Prediction in Crowded Highway Scenarios Using Bird Eye View Representations and CNNs","date":"2020-08-26","arxiv_id":"2008.11493","n_code_links":0,"syntology":null},{"paper":"/paper/deep-active-learning-in-remote-sensing-for","slug":"deep-active-learning-in-remote-sensing-for","title":"Deep Active Learning in Remote Sensing for data efficient Change Detection","date":"2020-08-25","arxiv_id":"2008.11201","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["previtus/ChangeDetectionProject"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/fastsal-a-computationally-efficient-network","slug":"fastsal-a-computationally-efficient-network","title":"FastSal: a Computationally Efficient Network for Visual Saliency Prediction","date":"2020-08-25","arxiv_id":"2008.11151","n_code_links":1,"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":["feiyanhu/FastSal"],"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":["official"]}}},{"paper":null,"slug":"on-estimating-gaze-by-self-attention","title":"On estimating gaze by self-attention augmented convolutions","date":"2020-08-25","arxiv_id":"2008.11055","n_code_links":0,"syntology":null},{"paper":null,"slug":"page-a-simple-and-optimal-probabilistic","title":"PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization","date":"2020-08-25","arxiv_id":"2008.10898","n_code_links":0,"syntology":null},{"paper":"/paper/tornado-net-multiview-total-variation","slug":"tornado-net-multiview-total-variation","title":"TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond inceptiOn module","date":"2020-08-24","arxiv_id":"2008.10544","n_code_links":0,"syntology":null},{"paper":"/paper/m2caiseg-semantic-segmentation-of","slug":"m2caiseg-semantic-segmentation-of","title":"m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks","date":"2020-08-23","arxiv_id":"2008.10134","n_code_links":1,"syntology":null},{"paper":"/paper/an-improved-person-re-identification-method","slug":"an-improved-person-re-identification-method","title":"An Improved Person Re-identification Method by light-weight convolutional neural network","date":"2020-08-21","arxiv_id":"2008.09448","n_code_links":1,"syntology":null},{"paper":"/paper/awnet-attentive-wavelet-network-for-image-isp","slug":"awnet-attentive-wavelet-network-for-image-isp","title":"AWNet: Attentive Wavelet Network for Image ISP","date":"2020-08-20","arxiv_id":"2008.09228","n_code_links":1,"syntology":null},{"paper":"/paper/mmea-entity-alignment-for-multi-modal","slug":"mmea-entity-alignment-for-multi-modal","title":"MMEA: Entity Alignment for Multi-Modal Knowledge Graphs","date":"2020-08-20","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/monocular-expressive-body-regression-through","slug":"monocular-expressive-body-regression-through","title":"Monocular Expressive Body Regression through Body-Driven Attention","date":"2020-08-20","arxiv_id":"2008.09062","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["vchoutas/expose"],"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":["official"]}}},{"paper":"/paper/on-transversality-of-bent-hyperplane","slug":"on-transversality-of-bent-hyperplane","title":"On transversality of bent hyperplane arrangements and the topological expressiveness of ReLU neural networks","date":"2020-08-20","arxiv_id":"2008.09052","n_code_links":1,"syntology":null},{"paper":"/paper/direct-adversarial-training-for-gans","slug":"direct-adversarial-training-for-gans","title":"A New Perspective on Stabilizing GANs training: Direct Adversarial Training","date":"2020-08-19","arxiv_id":"2008.09041","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":3,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["iceli1007/DAT-GAN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"name-that-manufacturer-relating-image","title":"\"Name that manufacturer\". Relating image acquisition bias with task complexity when training deep learning models: experiments on head CT","date":"2020-08-19","arxiv_id":"2008.08525","n_code_links":0,"syntology":null},{"paper":null,"slug":"relu-activated-multi-layer-neural-networks","title":"ReLU activated Multi-Layer Neural Networks trained with Mixed Integer Linear Programs","date":"2020-08-19","arxiv_id":"2008.08386","n_code_links":0,"syntology":null},{"paper":null,"slug":"slide-free-muse-microscopy-to-h-e-histology","title":"Slide-free MUSE Microscopy to H&E Histology Modality Conversion via Unpaired Image-to-Image Translation GAN Models","date":"2020-08-19","arxiv_id":"2008.08579","n_code_links":0,"syntology":null},{"paper":"/paper/cinc-gan-for-effective-f0-prediction-for","slug":"cinc-gan-for-effective-f0-prediction-for","title":"CinC-GAN for Effective F0 prediction for Whisper-to-Normal Speech Conversion","date":"2020-08-18","arxiv_id":"2008.07788","n_code_links":1,"syntology":null},{"paper":null,"slug":"discovering-multi-hardware-mobile-models-via","title":"Discovering Multi-Hardware Mobile Models via Architecture Search","date":"2020-08-18","arxiv_id":"2008.08178","n_code_links":0,"syntology":null},{"paper":null,"slug":"multilanguage-number-plate-detection-using","title":"Multilanguage Number Plate Detection using Convolutional Neural Networks","date":"2020-08-18","arxiv_id":"2008.08023","n_code_links":0,"syntology":null},{"paper":null,"slug":"pc-u-net-learning-to-jointly-reconstruct-and","title":"PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data","date":"2020-08-18","arxiv_id":"2008.08194","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-based-source-separation-applied","title":"Deep Learning Based Source Separation Applied To Choir Ensembles","date":"2020-08-17","arxiv_id":"2008.07645","n_code_links":0,"syntology":null},{"paper":"/paper/deepgin-deep-generative-inpainting-network","slug":"deepgin-deep-generative-inpainting-network","title":"DeepGIN: Deep Generative Inpainting Network for Extreme Image Inpainting","date":"2020-08-17","arxiv_id":"2008.07173","n_code_links":1,"syntology":null},{"paper":"/paper/how-to-train-your-robust-human-pose-estimator","slug":"how-to-train-your-robust-human-pose-estimator","title":"AID: Pushing the Performance Boundary of Human Pose Estimation with Information Dropping Augmentation","date":"2020-08-17","arxiv_id":"2008.07139","n_code_links":2,"syntology":null},{"paper":null,"slug":"improving-emergency-response-during-hurricane","title":"Improving Emergency Response during Hurricane Season using Computer Vision","date":"2020-08-17","arxiv_id":"2008.07418","n_code_links":0,"syntology":null},{"paper":"/paper/learning-two-layer-residual-networks-with","slug":"learning-two-layer-residual-networks-with","title":"Nonparametric Learning of Two-Layer ReLU Residual Units","date":"2020-08-17","arxiv_id":"2008.07648","n_code_links":1,"syntology":null},{"paper":"/paper/glod-gaussian-likelihood-out-of-distribution","slug":"glod-gaussian-likelihood-out-of-distribution","title":"FOOD: Fast Out-Of-Distribution Detector","date":"2020-08-16","arxiv_id":"2008.06856","n_code_links":1,"syntology":null},{"paper":null,"slug":"spontaneous-preterm-birth-prediction-using","title":"Spontaneous preterm birth prediction using convolutional neural networks","date":"2020-08-16","arxiv_id":"2008.07000","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-detection-of-cortical-lesions-in","title":"Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI","date":"2020-08-15","arxiv_id":"2008.06780","n_code_links":0,"syntology":null},{"paper":"/paper/model-patching-closing-the-subgroup","slug":"model-patching-closing-the-subgroup","title":"Model Patching: Closing the Subgroup Performance Gap with Data Augmentation","date":"2020-08-15","arxiv_id":"2008.06775","n_code_links":1,"syntology":{"ran":18,"of":24,"n_ran_checked":17,"n_instrument":1,"unverified":6,"pointer_only":0,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 1 where Syntology's instrument failed) · 6 unverified","official":{"repos":["HazyResearch/model-patching"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":17,"n_unverified":6,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"analytical-bounds-on-the-local-lipschitz","title":"Analytical bounds on the local Lipschitz constants of affine-ReLU functions","date":"2020-08-14","arxiv_id":"2008.06141","n_code_links":0,"syntology":null},{"paper":null,"slug":"feedback-attention-for-cell-image","title":"Feedback Attention for Cell Image Segmentation","date":"2020-08-14","arxiv_id":"2008.06474","n_code_links":0,"syntology":null},{"paper":null,"slug":"adain-switchable-cyclegan-for-efficient","title":"AdaIN-Switchable CycleGAN for Efficient Unsupervised Low-Dose CT Denoising","date":"2020-08-13","arxiv_id":"2008.05753","n_code_links":0,"syntology":null},{"paper":"/paper/enhancing-speech-intelligibility-in-text-to","slug":"enhancing-speech-intelligibility-in-text-to","title":"Enhancing Speech Intelligibility in Text-To-Speech Synthesis using Speaking Style Conversion","date":"2020-08-13","arxiv_id":"2008.05809","n_code_links":1,"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: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/powers-of-layers-for-image-to-image","slug":"powers-of-layers-for-image-to-image","title":"Powers of layers for image-to-image translation","date":"2020-08-13","arxiv_id":"2008.05763","n_code_links":0,"syntology":null},{"paper":"/paper/an-ensemble-of-simple-convolutional-neural","slug":"an-ensemble-of-simple-convolutional-neural","title":"An Ensemble of Simple Convolutional Neural Network Models for MNIST Digit Recognition","date":"2020-08-12","arxiv_id":"2008.10400","n_code_links":2,"syntology":null},{"paper":null,"slug":"bone-segmentation-in-contrast-enhanced-whole","title":"Bone Segmentation in Contrast Enhanced Whole-Body Computed Tomography","date":"2020-08-12","arxiv_id":"2008.05223","n_code_links":0,"syntology":null},{"paper":"/paper/facial-expression-recognition-under-partial","slug":"facial-expression-recognition-under-partial","title":"Facial Expression Recognition Under Partial Occlusion from Virtual Reality Headsets based on Transfer Learning","date":"2020-08-12","arxiv_id":"2008.05563","n_code_links":1,"syntology":null},{"paper":null,"slug":"leveraging-automated-mixed-low-precision","title":"Leveraging Automated Mixed-Low-Precision Quantization for tiny edge microcontrollers","date":"2020-08-12","arxiv_id":"2008.05124","n_code_links":0,"syntology":null},{"paper":null,"slug":"pixel-level-corrosion-detection-on-metal","title":"Pixel-level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation","date":"2020-08-12","arxiv_id":"2008.05204","n_code_links":0,"syntology":null},{"paper":"/paper/uncertainty-quantification-using-variational","slug":"uncertainty-quantification-using-variational","title":"Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation","date":"2020-08-12","arxiv_id":"2008.07588","n_code_links":1,"syntology":null},{"paper":"/paper/using-convolution-neural-networks-to-learn","slug":"using-convolution-neural-networks-to-learn","title":"Enhancing Fiber Orientation Distributions using convolutional Neural Networks","date":"2020-08-12","arxiv_id":"2008.05409","n_code_links":1,"syntology":null},{"paper":null,"slug":"gelato-generative-latent-textured-objects","title":"GeLaTO: Generative Latent Textured Objects","date":"2020-08-11","arxiv_id":"2008.04852","n_code_links":0,"syntology":null},{"paper":null,"slug":"modeling-prosodic-phrasing-with-multi-task","title":"Modeling Prosodic Phrasing with Multi-Task Learning in Tacotron-based TTS","date":"2020-08-11","arxiv_id":"2008.05284","n_code_links":0,"syntology":null},{"paper":null,"slug":"pneumoxttention-a-cnn-compensating-for-human","title":"PneumoXttention: A CNN compensating for Human Fallibility when Detecting Pneumonia through CXR images with Attention","date":"2020-08-11","arxiv_id":"2008.04907","n_code_links":0,"syntology":null},{"paper":null,"slug":"reinforced-wasserstein-training-for-severity","title":"Reinforced Wasserstein Training for Severity-Aware Semantic Segmentation in Autonomous Driving","date":"2020-08-11","arxiv_id":"2008.04751","n_code_links":0,"syntology":null},{"paper":null,"slug":"spectrum-and-prosody-conversion-for-cross","title":"Spectrum and Prosody Conversion for Cross-lingual Voice Conversion with CycleGAN","date":"2020-08-11","arxiv_id":"2008.04562","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-based-human-detection-for-uavs","title":"Deep Learning-based Human Detection for UAVs with Optical and Infrared Cameras: System and Experiments","date":"2020-08-10","arxiv_id":"2008.04197","n_code_links":0,"syntology":null},{"paper":"/paper/do-ideas-have-shape-plato-s-theory-of-forms","slug":"do-ideas-have-shape-plato-s-theory-of-forms","title":"Do ideas have shape? Idea registration as the continuous limit of artificial neural networks","date":"2020-08-10","arxiv_id":"2008.03920","n_code_links":1,"syntology":null},{"paper":null,"slug":"diet-snn-direct-input-encoding-with-leakage","title":"DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks","date":"2020-08-09","arxiv_id":"2008.03658","n_code_links":0,"syntology":null},{"paper":"/paper/spatiotemporal-contrastive-video","slug":"spatiotemporal-contrastive-video","title":"Spatiotemporal Contrastive Video Representation Learning","date":"2020-08-09","arxiv_id":"2008.03800","n_code_links":4,"syntology":null},{"paper":"/paper/speaker-conditional-wavernn-towards-universal","slug":"speaker-conditional-wavernn-towards-universal","title":"Speaker Conditional WaveRNN: Towards Universal Neural Vocoder for Unseen Speaker and Recording Conditions","date":"2020-08-09","arxiv_id":"2008.05289","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/speedyspeech-efficient-neural-speech","slug":"speedyspeech-efficient-neural-speech","title":"SpeedySpeech: Efficient Neural Speech Synthesis","date":"2020-08-09","arxiv_id":"2008.03802","n_code_links":3,"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":["janvainer/speedyspeech"],"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":["official"]}}},{"paper":"/paper/unsupervised-feature-learning-by-cross-level","slug":"unsupervised-feature-learning-by-cross-level","title":"Unsupervised Feature Learning by Cross-Level Instance-Group Discrimination","date":"2020-08-09","arxiv_id":"2008.03813","n_code_links":2,"syntology":{"ran":6,"of":8,"n_ran_checked":4,"n_instrument":2,"unverified":2,"pointer_only":1,"phrase":"6 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; 2 where Syntology's instrument failed) · 2 unverified","official":{"repos":["frank-xwang/CLD-UnsupervisedLearning"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/audio-spoofing-verification-using-deep","slug":"audio-spoofing-verification-using-deep","title":"Audio Spoofing Verification using Deep Convolutional Neural Networks by Transfer Learning","date":"2020-08-08","arxiv_id":"2008.03464","n_code_links":1,"syntology":null},{"paper":null,"slug":"multimodal-image-to-image-translation-via","title":"Multimodal Image-to-Image Translation via Mutual Information Estimation and Maximization","date":"2020-08-08","arxiv_id":"2008.03529","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-lossless-binary-convolutional-neural","title":"Towards Lossless Binary Convolutional Neural Networks Using Piecewise Approximation","date":"2020-08-08","arxiv_id":"2008.03520","n_code_links":0,"syntology":null},{"paper":null,"slug":"unravelling-small-sample-size-problems-in-the","title":"Unravelling Small Sample Size Problems in the Deep Learning World","date":"2020-08-08","arxiv_id":"2008.03522","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-pspnet-and-unet-to-analyze-the-internal","title":"Exploring the parameter reusability of CNN","date":"2020-08-08","arxiv_id":"2008.03411","n_code_links":0,"syntology":null},{"paper":"/paper/a-multi-task-learning-approach-for-human","slug":"a-multi-task-learning-approach-for-human","title":"A Multi-Task Learning Approach for Human Activity Segmentation and Ergonomics Risk Assessment","date":"2020-08-07","arxiv_id":"2008.03014","n_code_links":1,"syntology":null},{"paper":"/paper/few-shot-learning-framework-to-reduce-inter","slug":"few-shot-learning-framework-to-reduce-inter","title":"Few Shot Learning Framework to Reduce Inter-observer Variability in Medical Images","date":"2020-08-07","arxiv_id":"2008.02952","n_code_links":3,"syntology":null},{"paper":null,"slug":"improve-generalization-and-robustness-of","title":"Improve Generalization and Robustness of Neural Networks via Weight Scale Shifting Invariant Regularizations","date":"2020-08-07","arxiv_id":"2008.02965","n_code_links":0,"syntology":null},{"paper":"/paper/the-ensemble-method-for-thorax-diseases","slug":"the-ensemble-method-for-thorax-diseases","title":"An Aggregate Method for Thorax Diseases Classification","date":"2020-08-07","arxiv_id":"2008.03008","n_code_links":1,"syntology":null},{"paper":null,"slug":"relu-nets-adapt-to-intrinsic-dimensionality","title":"A deep network construction that adapts to intrinsic dimensionality beyond the domain","date":"2020-08-06","arxiv_id":"2008.02545","n_code_links":0,"syntology":null},{"paper":null,"slug":"structured-convolutions-for-efficient-neural","title":"Structured Convolutions for Efficient Neural Network Design","date":"2020-08-06","arxiv_id":"2008.02454","n_code_links":0,"syntology":null},{"paper":"/paper/content-based-singing-voice-source-separation","slug":"content-based-singing-voice-source-separation","title":"Content based singing voice source separation via strong conditioning using aligned phonemes","date":"2020-08-05","arxiv_id":"2008.02070","n_code_links":1,"syntology":null},{"paper":"/paper/continuous-in-depth-neural-networks","slug":"continuous-in-depth-neural-networks","title":"Continuous-in-Depth Neural Networks","date":"2020-08-05","arxiv_id":"2008.02389","n_code_links":4,"syntology":{"ran":4,"of":6,"n_ran_checked":0,"n_instrument":4,"unverified":2,"pointer_only":6,"phrase":"4 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; 4 where Syntology's instrument failed) · 2 unverified","official":{"repos":["afqueiruga/ContinuousNet"],"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/global-voxel-transformer-networks-for","slug":"global-voxel-transformer-networks-for","title":"Global Voxel Transformer Networks for Augmented Microscopy","date":"2020-08-05","arxiv_id":"2008.02340","n_code_links":1,"syntology":null}],"record_sha256":"5c788a186de7ab7d1d6333d73ef2ebd7e7a90bffeb8485f9f14eee56fe764dcb","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}