{"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/53","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":53,"pages_in_order":104,"rows_per_page":100,"rows":[5201,5300],"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/52","next":"/method/relu/papers/54","papers":[{"paper":null,"slug":"icodenet-a-hierarchical-neural-network","title":"ICodeNet -- A Hierarchical Neural Network Approach for Source Code Author Identification","date":"2021-01-30","arxiv_id":"2102.00230","n_code_links":0,"syntology":null},{"paper":"/paper/segmentation-of-skin-lesions-and-their","slug":"segmentation-of-skin-lesions-and-their","title":"Segmentation of skin lesions and their attributes using Generative Adversarial Networks","date":"2021-01-30","arxiv_id":"2102.00169","n_code_links":1,"syntology":null},{"paper":null,"slug":"size-and-depth-separation-in-approximating","title":"Size and Depth Separation in Approximating Benign Functions with Neural Networks","date":"2021-01-30","arxiv_id":"2102.00314","n_code_links":0,"syntology":null},{"paper":null,"slug":"stay-alive-with-many-options-a-reinforcement","title":"Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm","date":"2021-01-30","arxiv_id":"2102.00168","n_code_links":0,"syntology":null},{"paper":null,"slug":"automated-deep-learning-analysis-of","title":"Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease","date":"2021-01-29","arxiv_id":"2101.12505","n_code_links":0,"syntology":null},{"paper":null,"slug":"between-steps-intermediate-relaxations","title":"Between steps: Intermediate relaxations between big-M and convex hull formulations","date":"2021-01-29","arxiv_id":"2101.12708","n_code_links":0,"syntology":null},{"paper":"/paper/capsnet-regularization-and-its-conjugation","slug":"capsnet-regularization-and-its-conjugation","title":"CapsNet Regularization and its Conjugation with ResNet for Signature Identification","date":"2021-01-29","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-threshold-attention-u-net-mtau-based","title":"Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans","date":"2021-01-29","arxiv_id":"2101.12404","n_code_links":0,"syntology":null},{"paper":null,"slug":"wavelet-denoised-resnet-cnn-and-lightgbm","title":"Wavelet Denoised-ResNet CNN and LightGBM Method to Predict Forex Rate of Change","date":"2021-01-29","arxiv_id":"2102.04861","n_code_links":0,"syntology":null},{"paper":"/paper/exploring-cross-image-pixel-contrast-for","slug":"exploring-cross-image-pixel-contrast-for","title":"Exploring Cross-Image Pixel Contrast for Semantic Segmentation","date":"2021-01-28","arxiv_id":"2101.11939","n_code_links":5,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":{"repos":["tfzhou/ContrastiveSeg"],"state":"official: not harvested","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":[]}}},{"paper":null,"slug":"reducing-relu-count-for-privacy-preserving","title":"Reducing ReLU Count for Privacy-Preserving CNN Speedup","date":"2021-01-28","arxiv_id":"2101.11835","n_code_links":0,"syntology":null},{"paper":null,"slug":"s-a-fast-and-deployable-secure-computation","title":"S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training","date":"2021-01-28","arxiv_id":"2101.12078","n_code_links":0,"syntology":null},{"paper":"/paper/tokens-to-token-vit-training-vision","slug":"tokens-to-token-vit-training-vision","title":"Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet","date":"2021-01-28","arxiv_id":"2101.11986","n_code_links":13,"syntology":{"ran":21,"of":26,"n_ran_checked":21,"n_instrument":0,"unverified":5,"pointer_only":8,"phrase":"21 ran (of which 16 constructed an object rather than computing a result; 21 with no instrument failure: 1 honoured, 0 violated, 20 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","official":{"repos":["yitu-opensource/T2T-ViT"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"paper":"/paper/bottleneck-transformers-for-visual","slug":"bottleneck-transformers-for-visual","title":"Bottleneck Transformers for Visual Recognition","date":"2021-01-27","arxiv_id":"2101.11605","n_code_links":13,"syntology":{"ran":26,"of":49,"n_ran_checked":19,"n_instrument":7,"unverified":23,"pointer_only":8,"phrase":"26 ran (of which 9 constructed an object rather than computing a result; 19 with no instrument failure: 1 honoured, 0 violated, 18 with no contract checked; 7 where Syntology's instrument failed) · 23 unverified","official":null}},{"paper":null,"slug":"effects-of-image-size-on-deep-learning","title":"Effects of Image Size on Deep Learning","date":"2021-01-27","arxiv_id":"2101.11508","n_code_links":0,"syntology":null},{"paper":"/paper/multi-hypothesis-pose-networks-rethinking-top","slug":"multi-hypothesis-pose-networks-rethinking-top","title":"Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation","date":"2021-01-27","arxiv_id":"2101.11223","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 1 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; the one sample that ran constructed an object rather than computing a result","official":{"repos":["rawalkhirodkar/MIPNet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/offcon-3-what-is-state-of-the-art-anyway","slug":"offcon-3-what-is-state-of-the-art-anyway","title":"OffCon$^3$: What is state of the art anyway?","date":"2021-01-27","arxiv_id":"2101.11331","n_code_links":1,"syntology":null},{"paper":null,"slug":"boosting-segmentation-performance-across","title":"Boosting Segmentation Performance across datasets using histogram specification with application to pelvic bone segmentation","date":"2021-01-26","arxiv_id":"2101.11135","n_code_links":0,"syntology":null},{"paper":"/paper/malware-detection-using-frequency-domain","slug":"malware-detection-using-frequency-domain","title":"Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning","date":"2021-01-26","arxiv_id":"2101.10578","n_code_links":1,"syntology":null},{"paper":null,"slug":"3d-u-net-for-segmentation-of-covid-19","title":"3D U-Net for segmentation of COVID-19 associated pulmonary infiltrates using transfer learning: State-of-the-art results on affordable hardware","date":"2021-01-25","arxiv_id":"2101.09976","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-new-approach-to-extracting-coronary","title":"A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms","date":"2021-01-25","arxiv_id":"2101.09848","n_code_links":0,"syntology":null},{"paper":"/paper/learning-structral-coherence-via-generative","slug":"learning-structral-coherence-via-generative","title":"Learning Structral coherence Via Generative Adversarial Network for Single Image Super-Resolution","date":"2021-01-25","arxiv_id":"2101.10165","n_code_links":1,"syntology":null},{"paper":null,"slug":"spatio-temporal-data-augmentation-for-visual","title":"Spatio-temporal Data Augmentation for Visual Surveillance","date":"2021-01-25","arxiv_id":"2101.09895","n_code_links":0,"syntology":null},{"paper":null,"slug":"cgans-for-cartoon-to-real-life-images","title":"cGANs for Cartoon to Real-life Images","date":"2021-01-24","arxiv_id":"2101.09793","n_code_links":0,"syntology":null},{"paper":"/paper/densenet-for-breast-tumor-classification-in","slug":"densenet-for-breast-tumor-classification-in","title":"DenseNet for Breast Tumor Classification in Mammographic Images","date":"2021-01-24","arxiv_id":"2101.09637","n_code_links":2,"syntology":null},{"paper":null,"slug":"gst-group-sparse-training-for-accelerating","title":"GST: Group-Sparse Training for Accelerating Deep Reinforcement Learning","date":"2021-01-24","arxiv_id":"2101.09650","n_code_links":0,"syntology":null},{"paper":"/paper/learning-synthetic-environments-for","slug":"learning-synthetic-environments-for","title":"Learning Synthetic Environments for Reinforcement Learning with Evolution Strategies","date":"2021-01-24","arxiv_id":"2101.09721","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-the-proof-of-global-convergence-of","title":"On the Proof of Global Convergence of Gradient Descent for Deep ReLU Networks with Linear Widths","date":"2021-01-24","arxiv_id":"2101.09612","n_code_links":0,"syntology":null},{"paper":"/paper/towards-robust-visual-information-extraction","slug":"towards-robust-visual-information-extraction","title":"Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution","date":"2021-01-24","arxiv_id":"2102.06732","n_code_links":1,"syntology":null},{"paper":null,"slug":"arabic-aspect-based-sentiment-analysis-using","title":"Arabic aspect based sentiment analysis using bidirectional GRU based models","date":"2021-01-23","arxiv_id":"2101.10539","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-cerebral-vessel-extraction-in-tof","title":"Automatic Cerebral Vessel Extraction in TOF-MRA Using Deep Learning","date":"2021-01-22","arxiv_id":"2101.09253","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-volumetric-segmentation-of-additive","title":"Automatic Volumetric Segmentation of Additive Manufacturing Defects with 3D U-Net","date":"2021-01-22","arxiv_id":"2101.08993","n_code_links":0,"syntology":null},{"paper":null,"slug":"expression-recognition-analysis-in-the-wild","title":"Expression Recognition Analysis in the Wild","date":"2021-01-22","arxiv_id":"2101.09231","n_code_links":0,"syntology":null},{"paper":null,"slug":"partition-based-convex-relaxations-for","title":"Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification","date":"2021-01-22","arxiv_id":"2101.09306","n_code_links":0,"syntology":null},{"paper":null,"slug":"solving-the-same-different-task-with","title":"Solving the Same-Different Task with Convolutional Neural Networks","date":"2021-01-22","arxiv_id":"2101.09129","n_code_links":0,"syntology":null},{"paper":null,"slug":"study-of-pre-processing-defenses-against","title":"Study of Pre-processing Defenses against Adversarial Attacks on State-of-the-art Speaker Recognition Systems","date":"2021-01-22","arxiv_id":"2101.08909","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-fully-rigorous-proof-of-the-derivation-of","title":"A Fully Rigorous Proof of the Derivation of Xavier and He's Initialization for Deep ReLU Networks","date":"2021-01-21","arxiv_id":"2101.12017","n_code_links":0,"syntology":null},{"paper":"/paper/analysis-of-information-flow-through-u-nets","slug":"analysis-of-information-flow-through-u-nets","title":"Analysis of Information Flow Through U-Nets","date":"2021-01-21","arxiv_id":"2101.08427","n_code_links":1,"syntology":null},{"paper":"/paper/analyzing-epistemic-and-aleatoric-uncertainty","slug":"analyzing-epistemic-and-aleatoric-uncertainty","title":"Analyzing Epistemic and Aleatoric Uncertainty for Drusen Segmentation in Optical Coherence Tomography Images","date":"2021-01-21","arxiv_id":"2101.08888","n_code_links":1,"syntology":null},{"paper":"/paper/characterizing-signal-propagation-to-close-1","slug":"characterizing-signal-propagation-to-close-1","title":"Characterizing signal propagation to close the performance gap in unnormalized ResNets","date":"2021-01-21","arxiv_id":"2101.08692","n_code_links":4,"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":["deepmind/deepmind-research"],"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/daf-re-a-challenging-crowd-sourced-large","slug":"daf-re-a-challenging-crowd-sourced-large","title":"DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition","date":"2021-01-21","arxiv_id":"2101.08674","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":["arkel23/animesion"],"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":"fwb-net-front-white-balance-network-for-color","title":"FWB-Net:Front White Balance Network for Color Shift Correction in Single Image Dehazing via Atmospheric Light Estimation","date":"2021-01-21","arxiv_id":"2101.08465","n_code_links":0,"syntology":null},{"paper":"/paper/ghostsr-learning-ghost-features-for-efficient","slug":"ghostsr-learning-ghost-features-for-efficient","title":"GhostSR: Learning Ghost Features for Efficient Image Super-Resolution","date":"2021-01-21","arxiv_id":"2101.08525","n_code_links":4,"syntology":null},{"paper":"/paper/ikshana-a-theory-of-human-scene-understanding","slug":"ikshana-a-theory-of-human-scene-understanding","title":"The Ikshana Hypothesis of Human Scene Understanding","date":"2021-01-21","arxiv_id":"2101.10837","n_code_links":2,"syntology":null},{"paper":null,"slug":"from-local-pseudorandom-generators-to","title":"From Local Pseudorandom Generators to Hardness of Learning","date":"2021-01-20","arxiv_id":"2101.08303","n_code_links":0,"syntology":null},{"paper":null,"slug":"splitsr-an-end-to-end-approach-to-super","title":"SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices","date":"2021-01-20","arxiv_id":"2101.07996","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-survey-on-shape-constraint-deep-learning","title":"A survey on shape-constraint deep learning for medical image segmentation","date":"2021-01-19","arxiv_id":"2101.07721","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-models-for-calculation-of","title":"Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly","date":"2021-01-19","arxiv_id":"2101.07606","n_code_links":0,"syntology":null},{"paper":"/paper/meningioma-segmentation-in-t1-weighted-mri","slug":"meningioma-segmentation-in-t1-weighted-mri","title":"Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms","date":"2021-01-19","arxiv_id":"2101.07715","n_code_links":1,"syntology":null},{"paper":null,"slug":"variance-based-samples-weighting-for","title":"Leveraging Local Variation in Data: Sampling and Weighting Schemes for Supervised Deep Learning","date":"2021-01-19","arxiv_id":"2101.07561","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-dnn-networks-using-un-rectifying","title":"Learning DNN networks using un-rectifying ReLU with compressed sensing application","date":"2021-01-18","arxiv_id":"2101.06940","n_code_links":0,"syntology":null},{"paper":null,"slug":"tlu-net-a-deep-learning-approach-for","title":"TLU-Net: A Deep Learning Approach for Automatic Steel Surface Defect Detection","date":"2021-01-18","arxiv_id":"2101.06915","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-relic-sketch-extraction-framework-based-on","title":"A relic sketch extraction framework based on detail-aware hierarchical deep network","date":"2021-01-17","arxiv_id":"2101.06616","n_code_links":0,"syntology":null},{"paper":null,"slug":"cost-efficient-online-hyperparameter","title":"Cost-Efficient Online Hyperparameter Optimization","date":"2021-01-17","arxiv_id":"2101.06590","n_code_links":0,"syntology":null},{"paper":null,"slug":"generating-attribution-maps-with-disentangled","title":"Generating Attribution Maps with Disentangled Masked Backpropagation","date":"2021-01-17","arxiv_id":"2101.06773","n_code_links":0,"syntology":null},{"paper":null,"slug":"adversarial-cycle-consistent-synthesis-of","title":"Adversarial cycle-consistent synthesis of cerebral microbleeds for data augmentation","date":"2021-01-16","arxiv_id":"2101.06468","n_code_links":0,"syntology":null},{"paper":null,"slug":"self-supervised-representation-learning-from-3","title":"Self-Supervised Representation Learning from Flow Equivariance","date":"2021-01-16","arxiv_id":"2101.06553","n_code_links":0,"syntology":null},{"paper":"/paper/deep-dual-resolution-networks-for-real-time","slug":"deep-dual-resolution-networks-for-real-time","title":"Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes","date":"2021-01-15","arxiv_id":"2101.06085","n_code_links":8,"syntology":null},{"paper":null,"slug":"towards-a-computed-aided-diagnosis-system-in","title":"Towards a Computed-Aided Diagnosis System in Colonoscopy: Automatic Polyp Segmentation Using Convolution Neural Networks","date":"2021-01-15","arxiv_id":"2101.06040","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multiple-classifier-approach-for","title":"A Multiple Classifier Approach for Concatenate-Designed Neural Networks","date":"2021-01-14","arxiv_id":"2101.05457","n_code_links":0,"syntology":null},{"paper":"/paper/fabricnet-a-fiber-recognition-architecture","slug":"fabricnet-a-fiber-recognition-architecture","title":"FabricNet: A Fiber Recognition Architecture Using Ensemble ConvNets","date":"2021-01-14","arxiv_id":"2101.05564","n_code_links":1,"syntology":null},{"paper":"/paper/gan-inversion-a-survey","slug":"gan-inversion-a-survey","title":"GAN Inversion: A Survey","date":"2021-01-14","arxiv_id":"2101.05278","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-lumen-segmentation-method-in-ureteroscopy","title":"A Lumen Segmentation Method in Ureteroscopy Images based on a Deep Residual U-Net architecture","date":"2021-01-13","arxiv_id":"2101.05021","n_code_links":0,"syntology":null},{"paper":null,"slug":"advancing-eosinophilic-esophagitis-diagnosis","title":"Advancing Eosinophilic Esophagitis Diagnosis and Phenotype Assessment with Deep Learning Computer Vision","date":"2021-01-13","arxiv_id":"2101.05326","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-based-prediction-of-alzheimer-s","title":"Deep learning based prediction of Alzheimer's disease from magnetic resonance images","date":"2021-01-13","arxiv_id":"2101.04961","n_code_links":0,"syntology":null},{"paper":"/paper/neural-sequence-to-grid-module-for-learning","slug":"neural-sequence-to-grid-module-for-learning","title":"Neural Sequence-to-grid Module for Learning Symbolic Rules","date":"2021-01-13","arxiv_id":"2101.04921","n_code_links":1,"syntology":null},{"paper":null,"slug":"whispered-and-lombard-neural-speech-synthesis","title":"Whispered and Lombard Neural Speech Synthesis","date":"2021-01-13","arxiv_id":"2101.05313","n_code_links":0,"syntology":null},{"paper":null,"slug":"cleftnet-augmented-deep-learning-for-synaptic","title":"CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy","date":"2021-01-12","arxiv_id":"2101.04266","n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-adversarial-u-net-for-domain-free","title":"Generative Adversarial U-Net for Domain-free Medical Image Augmentation","date":"2021-01-12","arxiv_id":"2101.04793","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-convergence-of-deep-networks-with","title":"A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks","date":"2021-01-12","arxiv_id":"2101.04243","n_code_links":0,"syntology":null},{"paper":null,"slug":"ufa-fuse-a-novel-deep-supervised-and-hybrid","title":"UFA-FUSE: A novel deep supervised and hybrid model for multi-focus image fusion","date":"2021-01-12","arxiv_id":"2101.04506","n_code_links":0,"syntology":null},{"paper":"/paper/action-priors-for-large-action-spaces-in","slug":"action-priors-for-large-action-spaces-in","title":"Action Priors for Large Action Spaces in Robotics","date":"2021-01-11","arxiv_id":"2101.04178","n_code_links":1,"syntology":null},{"paper":"/paper/repvgg-making-vgg-style-convnets-great-again","slug":"repvgg-making-vgg-style-convnets-great-again","title":"RepVGG: Making VGG-style ConvNets Great Again","date":"2021-01-11","arxiv_id":"2101.03697","n_code_links":25,"syntology":{"ran":13,"of":16,"n_ran_checked":8,"n_instrument":5,"unverified":3,"pointer_only":6,"phrase":"13 ran (of which 7 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","official":{"repos":["DingXiaoH/RepVGG"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["listed","official","unlocated"]}}},{"paper":"/paper/towards-real-world-blind-face-restoration","slug":"towards-real-world-blind-face-restoration","title":"Towards Real-World Blind Face Restoration with Generative Facial Prior","date":"2021-01-11","arxiv_id":"2101.04061","n_code_links":1,"syntology":null},{"paper":null,"slug":"accuracy-and-architecture-studies-of-residual","title":"Accuracy and Architecture Studies of Residual Neural Network solving Ordinary Differential Equations","date":"2021-01-10","arxiv_id":"2101.03583","n_code_links":0,"syntology":null},{"paper":"/paper/deep-reinforcement-learning-with-function","slug":"deep-reinforcement-learning-with-function","title":"Deep Reinforcement Learning with Function Properties in Mean Reversion Strategies","date":"2021-01-09","arxiv_id":"2101.03418","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploring-adversarial-fake-images-on-face","title":"Exploring Adversarial Fake Images on Face Manifold","date":"2021-01-09","arxiv_id":"2101.03272","n_code_links":0,"syntology":null},{"paper":null,"slug":"approaching-neural-network-uncertainty","title":"Approaching Neural Network Uncertainty Realism","date":"2021-01-08","arxiv_id":"2101.02974","n_code_links":0,"syntology":null},{"paper":null,"slug":"bayesian-u-net-for-segmenting-glaciers-in-sar","title":"Bayesian U-Net for Segmenting Glaciers in SAR Imagery","date":"2021-01-08","arxiv_id":"2101.03249","n_code_links":0,"syntology":null},{"paper":null,"slug":"glacier-calving-front-segmentation-using","title":"Glacier Calving Front Segmentation Using Attention U-Net","date":"2021-01-08","arxiv_id":"2101.03247","n_code_links":0,"syntology":null},{"paper":"/paper/monocular-depth-estimation-using-laplacian","slug":"monocular-depth-estimation-using-laplacian","title":"Monocular Depth Estimation Using Laplacian Pyramid-Based Depth Residuals","date":"2021-01-08","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"nvae-gan-based-approach-for-unsupervised-time","title":"NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection","date":"2021-01-08","arxiv_id":"2101.02908","n_code_links":0,"syntology":null},{"paper":null,"slug":"residual-networks-classify-inputs-based-on-1","title":"Residual networks classify inputs based on their neural transient dynamics","date":"2021-01-08","arxiv_id":"2101.03009","n_code_links":0,"syntology":null},{"paper":null,"slug":"synthetic-glacier-sar-image-generation-from","title":"Synthetic Glacier SAR Image Generation from Arbitrary Masks Using Pix2Pix Algorithm","date":"2021-01-08","arxiv_id":"2101.03252","n_code_links":0,"syntology":null},{"paper":"/paper/end-2-end-covid-19-detection-from-breath","slug":"end-2-end-covid-19-detection-from-breath","title":"End-2-End COVID-19 Detection from Breath & Cough Audio","date":"2021-01-07","arxiv_id":"2102.08359","n_code_links":2,"syntology":null},{"paper":"/paper/facial-expression-recognition-in-the-wild-via","slug":"facial-expression-recognition-in-the-wild-via","title":"Facial Expression Recognition in the Wild via Deep Attentive Center Loss","date":"2021-01-07","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"more-reliable-ai-solution-breast-ultrasound","title":"More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination","date":"2021-01-07","arxiv_id":"2101.02639","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-understanding-learning-in-neural","title":"Towards Understanding Learning in Neural Networks with Linear Teachers","date":"2021-01-07","arxiv_id":"2101.02533","n_code_links":0,"syntology":null},{"paper":null,"slug":"vhs-to-hdtv-video-translation-using-multi","title":"VHS to HDTV Video Translation using Multi-task Adversarial Learning","date":"2021-01-07","arxiv_id":"2101.02384","n_code_links":0,"syntology":null},{"paper":null,"slug":"cap-context-aware-pruning-for-semantic","title":"CAP-Context-Aware-Pruning-for-Semantic-Segmentation","date":"2021-01-06","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"cap-context-aware-pruning-for-semantic-1","title":"CAP: Context-Aware Pruning for Semantic-Segmentation","date":"2021-01-06","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/industrial-image-anomaly-localization-based","slug":"industrial-image-anomaly-localization-based","title":"Industrial Image Anomaly Localization Based on Gaussian Clustering of Pretrained Feature","date":"2021-01-06","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"isetauto-detecting-vehicles-with-depth-and","title":"ISETAuto: Detecting vehicles with depth and radiance information","date":"2021-01-06","arxiv_id":"2101.01843","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-automatic-system-to-monitor-the-physical","title":"An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic","date":"2021-01-05","arxiv_id":"2101.01373","n_code_links":0,"syntology":null},{"paper":null,"slug":"contextual-colorization-and-denoising-for-low","title":"Contextual colorization and denoising for low-light ultra high resolution sequences","date":"2021-01-05","arxiv_id":"2101.01597","n_code_links":0,"syntology":null},{"paper":"/paper/local-memory-attention-for-fast-video","slug":"local-memory-attention-for-fast-video","title":"Local Memory Attention for Fast Video Semantic Segmentation","date":"2021-01-05","arxiv_id":"2101.01715","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-framework-for-fast-scalable-bnn-inference","title":"A Framework for Fast Scalable BNN Inference using Googlenet and Transfer Learning","date":"2021-01-04","arxiv_id":"2101.00793","n_code_links":0,"syntology":null},{"paper":null,"slug":"low-light-image-enhancement-via-global-and","title":"Low Light Image Enhancement via Global and Local Context Modeling","date":"2021-01-04","arxiv_id":"2101.00850","n_code_links":0,"syntology":null},{"paper":"/paper/one-shot-model-for-the-prediction-of-covid-19","slug":"one-shot-model-for-the-prediction-of-covid-19","title":"One Shot Model For The Prediction of COVID-19 and Lesions Segmentation In Chest CT Scans Through The Affinity Among Lesion Mask Features","date":"2021-01-04","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/provable-generalization-of-sgd-trained-neural","slug":"provable-generalization-of-sgd-trained-neural","title":"Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise","date":"2021-01-04","arxiv_id":"2101.01152","n_code_links":1,"syntology":null}],"record_sha256":"cc4c2cb09084b9e8f9226479ecea4104577fc34d8f0238d6f0f9ac639554acbe","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}