{"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/1x1-convolution/papers/32","list_of":"/method/1x1-convolution","method":"1x1 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":32,"pages_in_order":57,"rows_per_page":100,"rows":[3101,3200],"of":5640,"counts":{"archive_papers_tagged":5640,"with_a_code_link":2516,"where_syntology_ran_a_sample":651,"not_listed_spam_title":0,"listed":5640,"listed_where_code_ran":651,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":545,"every_run_a_failure_of_syntologys_instrument":106,"listed_with_a_run_with_no_instrument_failure":545,"listed_every_run_a_failure_of_syntologys_instrument":106,"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/1x1-convolution","prev":"/method/1x1-convolution/papers/31","next":"/method/1x1-convolution/papers/33","papers":[{"paper":"/paper/histo-fetch-on-the-fly-processing-of","slug":"histo-fetch-on-the-fly-processing-of","title":"Histo-fetch -- On-the-fly processing of gigapixel whole slide images simplifies and speeds neural network training","date":"2021-02-23","arxiv_id":"2102.11433","n_code_links":1,"syntology":null},{"paper":"/paper/road-the-road-event-awareness-dataset-for","slug":"road-the-road-event-awareness-dataset-for","title":"ROAD: The ROad event Awareness Dataset for Autonomous Driving","date":"2021-02-23","arxiv_id":"2102.11585","n_code_links":3,"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":{"repos":["gurkirt/3D-RetinaNet","gurkirt/road-dataset"],"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/sise-pc-semi-supervised-image-subsampling-for","slug":"sise-pc-semi-supervised-image-subsampling-for","title":"SISE-PC: Semi-supervised Image Subsampling for Explainable Pathology","date":"2021-02-23","arxiv_id":"2102.11560","n_code_links":1,"syntology":null},{"paper":null,"slug":"wavelet-transform-analytics-for-rf-based-uav","title":"Wavelet Transform Analytics for RF-Based UAV Detection and Identification System Using Machine Learning","date":"2021-02-23","arxiv_id":"2102.11894","n_code_links":0,"syntology":null},{"paper":"/paper/cstr-a-classification-perspective-on-scene","slug":"cstr-a-classification-perspective-on-scene","title":"Revisiting Classification Perspective on Scene Text Recognition","date":"2021-02-22","arxiv_id":"2102.10884","n_code_links":1,"syntology":null},{"paper":"/paper/exploring-supervised-and-unsupervised-rewards","slug":"exploring-supervised-and-unsupervised-rewards","title":"Exploring Supervised and Unsupervised Rewards in Machine Translation","date":"2021-02-22","arxiv_id":"2102.11403","n_code_links":1,"syntology":null},{"paper":"/paper/lightweight-combinational-machine-learning","slug":"lightweight-combinational-machine-learning","title":"Lightweight Combinational Machine Learning Algorithm for Sorting Canine Torso Radiographs","date":"2021-02-22","arxiv_id":"2102.11385","n_code_links":1,"syntology":null},{"paper":null,"slug":"lottery-ticket-implies-accuracy-degradation","title":"Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?","date":"2021-02-19","arxiv_id":"2102.11068","n_code_links":0,"syntology":null},{"paper":"/paper/training-cascaded-networks-for-speeded","slug":"training-cascaded-networks-for-speeded","title":"Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss","date":"2021-02-19","arxiv_id":"2102.09808","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-mathematical-principle-of-deep-learning","title":"A Mathematical Principle of Deep Learning: Learn the Geodesic Curve in the Wasserstein Space","date":"2021-02-18","arxiv_id":"2102.09235","n_code_links":0,"syntology":null},{"paper":"/paper/densely-nested-top-down-flows-for-salient","slug":"densely-nested-top-down-flows-for-salient","title":"Densely Nested Top-Down Flows for Salient Object Detection","date":"2021-02-18","arxiv_id":"2102.09133","n_code_links":1,"syntology":null},{"paper":"/paper/recurrent-rational-networks","slug":"recurrent-rational-networks","title":"Adaptive Rational Activations to Boost Deep Reinforcement Learning","date":"2021-02-18","arxiv_id":"2102.09407","n_code_links":4,"syntology":{"ran":6,"of":6,"n_ran_checked":0,"n_instrument":6,"unverified":0,"pointer_only":3,"phrase":"6 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; 6 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ml-research/rational_activations","ml-research/rational_rl","ml-research/rational_sl","k4ntz/activation-functions"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-dataset-and-benchmark-for-malaria-life","title":"A Dataset and Benchmark for Malaria Life-Cycle Classification in Thin Blood Smear Images","date":"2021-02-17","arxiv_id":"2102.08708","n_code_links":0,"syntology":null},{"paper":null,"slug":"ensemble-transfer-learning-of-elastography","title":"Ensemble Transfer Learning of Elastography and B-mode Breast Ultrasound Images","date":"2021-02-17","arxiv_id":"2102.08567","n_code_links":0,"syntology":null},{"paper":"/paper/lambdanetworks-modeling-long-range-1","slug":"lambdanetworks-modeling-long-range-1","title":"LambdaNetworks: Modeling Long-Range Interactions Without Attention","date":"2021-02-17","arxiv_id":"2102.08602","n_code_links":7,"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":null}},{"paper":null,"slug":"rethinking-co-design-of-neural-architectures","title":"Rethinking Co-design of Neural Architectures and Hardware Accelerators","date":"2021-02-17","arxiv_id":"2102.08619","n_code_links":0,"syntology":null},{"paper":"/paper/an-automl-based-approach-to-multimodal-image","slug":"an-automl-based-approach-to-multimodal-image","title":"An AutoML-based Approach to Multimodal Image Sentiment Analysis","date":"2021-02-16","arxiv_id":"2102.08092","n_code_links":0,"syntology":null},{"paper":null,"slug":"complex-momentum-for-learning-in-games","title":"Complex Momentum for Optimization in Games","date":"2021-02-16","arxiv_id":"2102.08431","n_code_links":0,"syntology":null},{"paper":"/paper/feature-pyramid-network-with-multi-head","slug":"feature-pyramid-network-with-multi-head","title":"A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images","date":"2021-02-16","arxiv_id":"2102.07997","n_code_links":2,"syntology":null},{"paper":"/paper/improving-deep-learning-based-semi-supervised","slug":"improving-deep-learning-based-semi-supervised","title":"Comparison of semi-supervised deep learning algorithms for audio classification","date":"2021-02-16","arxiv_id":"2102.08183","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-stage-transmission-line-flow-control","title":"Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents","date":"2021-02-16","arxiv_id":"2102.08430","n_code_links":0,"syntology":null},{"paper":null,"slug":"training-larger-networks-for-deep","title":"Training Larger Networks for Deep Reinforcement Learning","date":"2021-02-16","arxiv_id":"2102.07920","n_code_links":0,"syntology":null},{"paper":null,"slug":"colored-kimia-path24-dataset-configurations","title":"Colored Kimia Path24 Dataset: Configurations and Benchmarks with Deep Embeddings","date":"2021-02-15","arxiv_id":"2102.07611","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-and-severity-classification-of","title":"Detection and severity classification of COVID-19 in CT images using deep learning","date":"2021-02-15","arxiv_id":"2102.07726","n_code_links":0,"syntology":null},{"paper":"/paper/momentum-residual-neural-networks","slug":"momentum-residual-neural-networks","title":"Momentum Residual Neural Networks","date":"2021-02-15","arxiv_id":"2102.07870","n_code_links":1,"syntology":null},{"paper":"/paper/fast-accurate-barcode-detection-in-ultra-high","slug":"fast-accurate-barcode-detection-in-ultra-high","title":"Fast, Accurate Barcode Detection in Ultra High-Resolution Images","date":"2021-02-13","arxiv_id":"2102.06868","n_code_links":0,"syntology":null},{"paper":null,"slug":"depthwise-separable-convolutions-allow-for","title":"Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization","date":"2021-02-12","arxiv_id":"2102.06496","n_code_links":0,"syntology":null},{"paper":"/paper/q-value-weighted-regression-reinforcement-1","slug":"q-value-weighted-regression-reinforcement-1","title":"Q-Value Weighted Regression: Reinforcement Learning with Limited Data","date":"2021-02-12","arxiv_id":"2102.06782","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"0 ran · 1 unverified","official":null}},{"paper":null,"slug":"aboships-an-inshore-and-offshore-maritime","title":"ABOShips -- An Inshore and Offshore Maritime Vessel Detection Dataset with Precise Annotations","date":"2021-02-11","arxiv_id":"2102.05869","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-deepsentinel-an-extensible-corpus-of","title":"Towards DeepSentinel: An extensible corpus of labelled Sentinel-1 and -2 imagery and a general-purpose sensor-fusion semantic embedding model","date":"2021-02-11","arxiv_id":"2102.06260","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-semantic-segmentation-by","slug":"unsupervised-semantic-segmentation-by","title":"Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals","date":"2021-02-11","arxiv_id":"2102.06191","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","official":{"repos":["wvangansbeke/Unsupervised-Semantic-Segmentation"],"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":["listed","official"]}}},{"paper":null,"slug":"adafuse-adaptive-temporal-fusion-network-for-1","title":"AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition","date":"2021-02-10","arxiv_id":"2102.05775","n_code_links":0,"syntology":null},{"paper":null,"slug":"application-of-yolo-on-mask-detection-task","title":"Application of Yolo on Mask Detection Task","date":"2021-02-10","arxiv_id":"2102.05402","n_code_links":0,"syntology":null},{"paper":"/paper/brecq-pushing-the-limit-of-post-training-1","slug":"brecq-pushing-the-limit-of-post-training-1","title":"BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction","date":"2021-02-10","arxiv_id":"2102.05426","n_code_links":3,"syntology":{"ran":5,"of":5,"n_ran_checked":2,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yhhhli/BRECQ"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"paper":"/paper/enhancing-real-world-adversarial-patches-with","slug":"enhancing-real-world-adversarial-patches-with","title":"Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target Scenes","date":"2021-02-10","arxiv_id":"2102.05334","n_code_links":1,"syntology":null},{"paper":"/paper/pruning-of-convolutional-neural-networks","slug":"pruning-of-convolutional-neural-networks","title":"Pruning of Convolutional Neural Networks Using Ising Energy Model","date":"2021-02-10","arxiv_id":"2102.05437","n_code_links":1,"syntology":null},{"paper":"/paper/regional-attention-with-architecture-rebuilt","slug":"regional-attention-with-architecture-rebuilt","title":"Regional Attention with Architecture-Rebuilt 3D Network for RGB-D Gesture Recognition","date":"2021-02-10","arxiv_id":"2102.05348","n_code_links":1,"syntology":null},{"paper":null,"slug":"searching-for-fast-model-families-on","title":"Searching for Fast Model Families on Datacenter Accelerators","date":"2021-02-10","arxiv_id":"2102.05610","n_code_links":0,"syntology":null},{"paper":null,"slug":"distribution-adaptive-int8-quantization-for","title":"Distribution Adaptive INT8 Quantization for Training CNNs","date":"2021-02-09","arxiv_id":"2102.04782","n_code_links":0,"syntology":null},{"paper":"/paper/mali-a-memory-efficient-and-reverse-accurate-1","slug":"mali-a-memory-efficient-and-reverse-accurate-1","title":"MALI: A memory efficient and reverse accurate integrator for Neural ODEs","date":"2021-02-09","arxiv_id":"2102.04668","n_code_links":1,"syntology":{"ran":2,"of":4,"n_ran_checked":2,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["juntang-zhuang/TorchDiffEqPack"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":"/paper/ranp-resource-aware-neuron-pruning-at-1","slug":"ranp-resource-aware-neuron-pruning-at-1","title":"RANP: Resource Aware Neuron Pruning at Initialization for 3D CNNs","date":"2021-02-09","arxiv_id":"2103.08457","n_code_links":1,"syntology":null},{"paper":null,"slug":"rmopp-robust-multi-objective-post-processing","title":"RMOPP: Robust Multi-Objective Post-Processing for Effective Object Detection","date":"2021-02-09","arxiv_id":"2102.04582","n_code_links":0,"syntology":null},{"paper":null,"slug":"train-a-one-million-way-instance-classifier","title":"Train a One-Million-Way Instance Classifier for Unsupervised Visual Representation Learning","date":"2021-02-09","arxiv_id":"2102.04848","n_code_links":0,"syntology":null},{"paper":"/paper/spike-based-residual-blocks","slug":"spike-based-residual-blocks","title":"Deep Residual Learning in Spiking Neural Networks","date":"2021-02-08","arxiv_id":"2102.04159","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"pointer_only":3,"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) · 1 unverified","official":{"repos":["fangwei123456/Spike-Element-Wise-ResNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["community"]}}},{"paper":null,"slug":"automatic-breast-lesion-detection-in","title":"Automatic Breast Lesion Detection in Ultrafast DCE-MRI Using Deep Learning","date":"2021-02-07","arxiv_id":"2102.03932","n_code_links":0,"syntology":null},{"paper":"/paper/self-supervised-driven-consistency-training","slug":"self-supervised-driven-consistency-training","title":"Self-supervised driven consistency training for annotation efficient histopathology image analysis","date":"2021-02-07","arxiv_id":"2102.03897","n_code_links":2,"syntology":null},{"paper":"/paper/active-slices-for-sliced-stein-discrepancy","slug":"active-slices-for-sliced-stein-discrepancy","title":"Active Slices for Sliced Stein Discrepancy","date":"2021-02-05","arxiv_id":"2102.03159","n_code_links":1,"syntology":{"ran":0,"of":1,"n_ran_checked":0,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"0 ran · 1 unverified","official":{"repos":["WenboGong/Sliced_Kernelized_Stein_Discrepancy"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":null,"slug":"convolutional-neural-network-interpretability","title":"Convolutional Neural Network Interpretability with General Pattern Theory","date":"2021-02-05","arxiv_id":"2102.04247","n_code_links":0,"syntology":null},{"paper":"/paper/gnn-rl-compression-topology-aware-network","slug":"gnn-rl-compression-topology-aware-network","title":"Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning","date":"2021-02-05","arxiv_id":"2102.03214","n_code_links":1,"syntology":{"ran":3,"of":4,"n_ran_checked":1,"n_instrument":2,"unverified":1,"pointer_only":4,"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) · 1 unverified","official":{"repos":["yusx-swapp/gnn-rl-model-compression"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":"/paper/on-the-connection-of-benford-s-law-and-neural","slug":"on-the-connection-of-benford-s-law-and-neural","title":"Rethinking Neural Networks With Benford's Law","date":"2021-02-05","arxiv_id":"2102.03313","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-cnns-for-large-scale-species","title":"Deep CNNs for large scale species classification","date":"2021-02-03","arxiv_id":"2102.01863","n_code_links":0,"syntology":null},{"paper":null,"slug":"robust-pedestrian-detection-in-thermal","title":"Robust pedestrian detection in thermal imagery using synthesized images","date":"2021-02-03","arxiv_id":"2102.02005","n_code_links":0,"syntology":null},{"paper":"/paper/anomalous-event-recognition-in-videos-based","slug":"anomalous-event-recognition-in-videos-based","title":"Anomalous Event Recognition in Videos Based on Joint Learningof Motion and Appearance with Multiple Ranking Measures","date":"2021-02-02","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"face-recognition-using-sf-3-cnn-with-higher","title":"Face Recognition Using $Sf_{3}CNN$ With Higher Feature Discrimination","date":"2021-02-02","arxiv_id":"2102.01404","n_code_links":0,"syntology":null},{"paper":"/paper/generating-images-from-caption-and-vice-versa","slug":"generating-images-from-caption-and-vice-versa","title":"Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search","date":"2021-02-02","arxiv_id":"2102.01645","n_code_links":3,"syntology":null},{"paper":"/paper/psla-improving-audio-event-classification","slug":"psla-improving-audio-event-classification","title":"PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation","date":"2021-02-02","arxiv_id":"2102.01243","n_code_links":1,"syntology":null},{"paper":"/paper/convnets-for-counting-object-detection-of","slug":"convnets-for-counting-object-detection-of","title":"ConvNets for Counting: Object Detection of Transient Phenomena in Steelpan Drums","date":"2021-02-01","arxiv_id":"2102.00632","n_code_links":1,"syntology":null},{"paper":null,"slug":"neural-network-architectures-to-classify","title":"Neural Network architectures to classify emotions in Indian Classical Music","date":"2021-02-01","arxiv_id":"2102.00616","n_code_links":0,"syntology":null},{"paper":null,"slug":"aacp-model-compression-by-accurate-and","title":"AACP: Model Compression by Accurate and Automatic Channel Pruning","date":"2021-01-31","arxiv_id":"2102.00390","n_code_links":0,"syntology":null},{"paper":"/paper/classification-of-fracture-and-normal","slug":"classification-of-fracture-and-normal","title":"Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models","date":"2021-01-31","arxiv_id":"2102.00515","n_code_links":0,"syntology":null},{"paper":"/paper/re-reproducibility-report-of-interpretable","slug":"re-reproducibility-report-of-interpretable","title":"[Re] Reproducibility report of \"Interpretable Complex-Valued Neural Networks for Privacy Protection\"","date":"2021-01-31","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-model-compression-based-on-the-training","title":"Deep Model Compression based on the Training History","date":"2021-01-30","arxiv_id":"2102.00160","n_code_links":0,"syntology":null},{"paper":"/paper/nl-cnn-a-resources-constrained-deep-learning","slug":"nl-cnn-a-resources-constrained-deep-learning","title":"NL-CNN: A Resources-Constrained Deep Learning Model based on Nonlinear Convolution","date":"2021-01-30","arxiv_id":"2102.00227","n_code_links":1,"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":"/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":"recssd-near-data-processing-for-solid-state","title":"RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference","date":"2021-01-29","arxiv_id":"2102.00075","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":"object-detection-made-simpler-by-eliminating","title":"Object Detection Made Simpler by Eliminating Heuristic NMS","date":"2021-01-28","arxiv_id":"2101.11782","n_code_links":0,"syntology":null},{"paper":null,"slug":"pig-net-inception-based-deep-learning","title":"PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation","date":"2021-01-28","arxiv_id":"2101.11987","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":"/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":"/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":"/paper/prototypical-pseudo-label-denoising-and","slug":"prototypical-pseudo-label-denoising-and","title":"Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation","date":"2021-01-26","arxiv_id":"2101.10979","n_code_links":2,"syntology":{"ran":8,"of":17,"n_ran_checked":6,"n_instrument":2,"unverified":9,"pointer_only":0,"phrase":"8 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 9 unverified","official":{"repos":["microsoft/ProDA"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":9,"ran_from_kinds":["official"]}}},{"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":"/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":"/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":"/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":"/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":"fooling-thermal-infrared-pedestrian-detectors","title":"Fooling thermal infrared pedestrian detectors in real world using small bulbs","date":"2021-01-20","arxiv_id":"2101.08154","n_code_links":0,"syntology":null},{"paper":"/paper/deep-convolutional-autoencoders-for","slug":"deep-convolutional-autoencoders-for","title":"Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brain","date":"2021-01-19","arxiv_id":null,"n_code_links":1,"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":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":"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":"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":"/paper/acp-automatic-channel-pruning-via-clustering","slug":"acp-automatic-channel-pruning-via-clustering","title":"ACP: Automatic Channel Pruning via Clustering and Swarm Intelligence Optimization for CNN","date":"2021-01-16","arxiv_id":"2101.06407","n_code_links":1,"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":null,"slug":"dynamic-normalization","title":"Dynamic Normalization","date":"2021-01-15","arxiv_id":"2101.06073","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":"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":null,"slug":"feature-refinement-an-expression-specific","title":"Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition","date":"2021-01-13","arxiv_id":"2101.04838","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":"towards-creating-a-deployable-grasp-type","title":"Towards Creating a Deployable Grasp Type Probability Estimator for a Prosthetic Hand","date":"2021-01-13","arxiv_id":"2101.05357","n_code_links":0,"syntology":null},{"paper":"/paper/lla-loss-aware-label-assignment-for-dense","slug":"lla-loss-aware-label-assignment-for-dense","title":"LLA: Loss-aware Label Assignment for Dense Pedestrian Detection","date":"2021-01-12","arxiv_id":"2101.04307","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"]}}}],"record_sha256":"f74d8db759b62463709187c1826a1e8263d28d1602850c2c3393f3c969dd100f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}