{"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/depthwise-convolution/papers/10","list_of":"/method/depthwise-convolution","method":"Depthwise 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":10,"pages_in_order":14,"rows_per_page":100,"rows":[901,1000],"of":1321,"counts":{"archive_papers_tagged":1321,"with_a_code_link":549,"where_syntology_ran_a_sample":141,"not_listed_spam_title":0,"listed":1321,"listed_where_code_ran":141,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":126,"every_run_a_failure_of_syntologys_instrument":15,"listed_with_a_run_with_no_instrument_failure":126,"listed_every_run_a_failure_of_syntologys_instrument":15,"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/depthwise-convolution","prev":"/method/depthwise-convolution/papers/9","next":"/method/depthwise-convolution/papers/11","papers":[{"paper":"/paper/cvt-introducing-convolutions-to-vision","slug":"cvt-introducing-convolutions-to-vision","title":"CvT: Introducing Convolutions to Vision Transformers","date":"2021-03-29","arxiv_id":"2103.15808","n_code_links":16,"syntology":{"ran":39,"of":47,"n_ran_checked":36,"n_instrument":3,"unverified":8,"pointer_only":8,"phrase":"39 ran (of which 19 constructed an object rather than computing a result; 36 with no instrument failure: 2 honoured, 0 violated, 34 with no contract checked; 3 where Syntology's instrument failed) · 8 unverified","official":{"repos":["microsoft/CvT"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":4,"ran_from_kinds":["listed","named_in_paper","official","unlocated"]}}},{"paper":null,"slug":"fixnorm-dissecting-weight-decay-for-training-1","title":"FixNorm: Dissecting Weight Decay for Training Deep Neural Networks","date":"2021-03-29","arxiv_id":"2103.15345","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-multi-objective-optimization-for","slug":"efficient-multi-objective-optimization-for","title":"Scalable Pareto Front Approximation for Deep Multi-Objective Learning","date":"2021-03-24","arxiv_id":"2103.13392","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":["ruchtem/cosmos"],"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/bossnas-exploring-hybrid-cnn-transformers","slug":"bossnas-exploring-hybrid-cnn-transformers","title":"BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search","date":"2021-03-23","arxiv_id":"2103.12424","n_code_links":1,"syntology":null},{"paper":null,"slug":"prediction-of-lung-and-colon-cancer-through","title":"Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps","date":"2021-03-22","arxiv_id":"2103.12155","n_code_links":0,"syntology":null},{"paper":null,"slug":"cascade-weight-shedding-in-deep-neural","title":"Cascade Weight Shedding in Deep Neural Networks: Benefits and Pitfalls for Network Pruning","date":"2021-03-19","arxiv_id":"2103.10629","n_code_links":0,"syntology":null},{"paper":"/paper/hw-nas-bench-hardware-aware-neural-1","slug":"hw-nas-bench-hardware-aware-neural-1","title":"HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark","date":"2021-03-19","arxiv_id":"2103.10584","n_code_links":1,"syntology":null},{"paper":"/paper/danish-fungi-2020-not-just-another-image","slug":"danish-fungi-2020-not-just-another-image","title":"Danish Fungi 2020 -- Not Just Another Image Recognition Dataset","date":"2021-03-18","arxiv_id":"2103.10107","n_code_links":1,"syntology":null},{"paper":null,"slug":"wheatnet-a-lightweight-convolutional-neural","title":"WheatNet: A Lightweight Convolutional Neural Network for High-throughput Image-based Wheat Head Detection and Counting","date":"2021-03-17","arxiv_id":"2103.09408","n_code_links":0,"syntology":null},{"paper":"/paper/edgecrnn-an-edgecomputing-oriented-model-of","slug":"edgecrnn-an-edgecomputing-oriented-model-of","title":"EdgeCRNN: an edgecomputing oriented model of acoustic feature enhancement for keyword spotting","date":"2021-03-14","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/revisiting-resnets-improved-training-and","slug":"revisiting-resnets-improved-training-and","title":"Revisiting ResNets: Improved Training and Scaling Strategies","date":"2021-03-13","arxiv_id":"2103.07579","n_code_links":3,"syntology":null},{"paper":"/paper/patchnet-short-range-template-matching-for","slug":"patchnet-short-range-template-matching-for","title":"PatchNet -- Short-range Template Matching for Efficient Video Processing","date":"2021-03-10","arxiv_id":"2103.07371","n_code_links":1,"syntology":null},{"paper":"/paper/enhancing-sensor-resolution-improves-cnn","slug":"enhancing-sensor-resolution-improves-cnn","title":"Enhancing sensor resolution improves CNN accuracy given the same number of parameters or FLOPS","date":"2021-03-09","arxiv_id":"2103.05251","n_code_links":1,"syntology":null},{"paper":null,"slug":"multitasking-deep-learning-model-for","title":"Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy","date":"2021-03-06","arxiv_id":"2103.04207","n_code_links":0,"syntology":null},{"paper":"/paper/webface260m-a-benchmark-unveiling-the-power","slug":"webface260m-a-benchmark-unveiling-the-power","title":"WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition","date":"2021-03-06","arxiv_id":"2103.04098","n_code_links":0,"syntology":null},{"paper":"/paper/coordinate-attention-for-efficient-mobile","slug":"coordinate-attention-for-efficient-mobile","title":"Coordinate Attention for Efficient Mobile Network Design","date":"2021-03-04","arxiv_id":"2103.02907","n_code_links":2,"syntology":null},{"paper":null,"slug":"sub-pixel-face-landmarks-using-heatmaps-and-a","title":"Sub-pixel face landmarks using heatmaps and a bag of tricks","date":"2021-03-04","arxiv_id":"2103.03059","n_code_links":0,"syntology":null},{"paper":null,"slug":"sensing-population-distribution-from","title":"Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases","date":"2021-03-03","arxiv_id":"2103.02155","n_code_links":0,"syntology":null},{"paper":"/paper/network-pruning-via-resource-reallocation","slug":"network-pruning-via-resource-reallocation","title":"Network Pruning via Resource Reallocation","date":"2021-03-02","arxiv_id":"2103.01847","n_code_links":1,"syntology":null},{"paper":null,"slug":"using-cnns-to-identify-the-origin-of-finger","title":"Using CNNs to Identify the Origin of Finger Vein Image","date":"2021-03-02","arxiv_id":"2103.01632","n_code_links":0,"syntology":null},{"paper":"/paper/deep-active-shape-model-for-face-alignment","slug":"deep-active-shape-model-for-face-alignment","title":"ASMNet: a Lightweight Deep Neural Network for Face Alignment and Pose Estimation","date":"2021-02-27","arxiv_id":"2103.00119","n_code_links":1,"syntology":null},{"paper":null,"slug":"robust-pollen-imagery-classification-with","title":"Robust Pollen Imagery Classification with Generative Modeling and Mixup Training","date":"2021-02-25","arxiv_id":"2102.13143","n_code_links":0,"syntology":null},{"paper":"/paper/do-we-really-need-explicit-position-encodings","slug":"do-we-really-need-explicit-position-encodings","title":"Conditional Positional Encodings for Vision Transformers","date":"2021-02-22","arxiv_id":"2102.10882","n_code_links":2,"syntology":null},{"paper":"/paper/efficient-two-stream-network-for-violence","slug":"efficient-two-stream-network-for-violence","title":"Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTM","date":"2021-02-21","arxiv_id":"2102.10590","n_code_links":1,"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":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":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":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":"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":"/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":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/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":"/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/ml-doctor-holistic-risk-assessment-of","slug":"ml-doctor-holistic-risk-assessment-of","title":"ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models","date":"2021-02-04","arxiv_id":"2102.02551","n_code_links":1,"syntology":{"ran":1,"of":3,"n_ran_checked":0,"n_instrument":1,"unverified":2,"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) · 2 unverified","official":{"repos":["liuyugeng/ml-doctor"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"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":"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/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":"/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/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":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":"dynamic-normalization","title":"Dynamic Normalization","date":"2021-01-15","arxiv_id":"2101.06073","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":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/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":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":"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":"a-half-space-stochastic-projected-gradient","title":"A Half-Space Stochastic Projected Gradient Method for Group Sparsity Regularization","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"boosting-certified-robustness-of-deep","title":"Boosting Certified Robustness of Deep Networks via a Compositional Architecture","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"differentiable-dynamic-wirings-for-neural","title":"Differentiable Dynamic Wirings for Neural Networks","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"generative-adversarial-neural-architecture","title":"Generative Adversarial Neural Architecture Search with Importance Sampling","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"hw-nas-bench-hardware-aware-neural","title":"HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-low-precision-network-quantization","title":"Improving Low-Precision Network Quantization via Bin Regularization","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"making-coherence-out-of-nothing-at-all-1","title":"Making Coherence Out of Nothing At All: Measuring Evolution of Gradient Alignment","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"nahas-neural-architecture-and-hardware","title":"NAHAS: Neural Architecture and Hardware Accelerator Search","date":"2021-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/kaleidoscope-an-efficient-learnable-1","slug":"kaleidoscope-an-efficient-learnable-1","title":"Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps","date":"2020-12-29","arxiv_id":"2012.14966","n_code_links":2,"syntology":{"ran":16,"of":23,"n_ran_checked":9,"n_instrument":7,"unverified":7,"pointer_only":0,"phrase":"16 ran (of which 9 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 7 where Syntology's instrument failed) · 7 unverified","official":{"repos":["HazyResearch/butterfly","HazyResearch/learning-circuits"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":9,"n_ran_no_instrument_failure":9,"n_unverified":7,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"direct-quantization-for-training-highly","title":"Direct Quantization for Training Highly Accurate Low Bit-width Deep Neural Networks","date":"2020-12-26","arxiv_id":"2012.13762","n_code_links":0,"syntology":null},{"paper":null,"slug":"diabetic-retinopathy-grading-system-based-on","title":"Diabetic Retinopathy Grading System Based on Transfer Learning","date":"2020-12-23","arxiv_id":"2012.12515","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-and-visualizable-convolutional","slug":"efficient-and-visualizable-convolutional","title":"Efficient and Visualizable Convolutional Neural Networks for COVID-19 Classification Using Chest CT","date":"2020-12-22","arxiv_id":"2012.11860","n_code_links":1,"syntology":null},{"paper":"/paper/fracbnn-accurate-and-fpga-efficient-binary","slug":"fracbnn-accurate-and-fpga-efficient-binary","title":"FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations","date":"2020-12-22","arxiv_id":"2012.12206","n_code_links":2,"syntology":null},{"paper":"/paper/objectron-a-large-scale-dataset-of-object","slug":"objectron-a-large-scale-dataset-of-object","title":"Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations","date":"2020-12-18","arxiv_id":"2012.09988","n_code_links":1,"syntology":null},{"paper":null,"slug":"transfer-learning-based-automatic-model","title":"Transfer Learning Based Automatic Model Creation Tool For Resource Constraint Devices","date":"2020-12-18","arxiv_id":"2012.10056","n_code_links":0,"syntology":null},{"paper":null,"slug":"distilling-optimal-neural-networks-rapid","title":"Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces","date":"2020-12-16","arxiv_id":"2012.08859","n_code_links":0,"syntology":null},{"paper":"/paper/hr-depth-high-resolution-self-supervised","slug":"hr-depth-high-resolution-self-supervised","title":"HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation","date":"2020-12-14","arxiv_id":"2012.07356","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: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["shawLyu/HR-Depth"],"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":null,"slug":"one-shot-learning-with-triplet-loss-for","title":"One-Shot Learning with Triplet Loss for Vegetation Classification Tasks","date":"2020-12-14","arxiv_id":"2012.07403","n_code_links":0,"syntology":null},{"paper":null,"slug":"minivlm-a-smaller-and-faster-vision-language","title":"MiniVLM: A Smaller and Faster Vision-Language Model","date":"2020-12-13","arxiv_id":"2012.06946","n_code_links":0,"syntology":null},{"paper":"/paper/simple-copy-paste-is-a-strong-data","slug":"simple-copy-paste-is-a-strong-data","title":"Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation","date":"2020-12-13","arxiv_id":"2012.07177","n_code_links":5,"syntology":{"ran":2,"of":2,"n_ran_checked":1,"n_instrument":1,"unverified":0,"pointer_only":0,"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":{"repos":["tensorflow/tpu"],"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":"a-multi-task-joint-framework-for-real-time","title":"A Multi-task Joint Framework for Real-time Person Search","date":"2020-12-11","arxiv_id":"2012.06418","n_code_links":0,"syntology":null},{"paper":"/paper/ensemble-cvdnet-a-deep-learning-based-end-to","slug":"ensemble-cvdnet-a-deep-learning-based-end-to","title":"Ensemble-CVDNet: A Deep Learning based End-to-End Classification Framework for COVID-19 Detection using Ensembles of Networks","date":"2020-12-09","arxiv_id":"2012.09132","n_code_links":1,"syntology":null},{"paper":null,"slug":"efficient-human-pose-estimation-with","title":"Efficient Human Pose Estimation with Depthwise Separable Convolution and Person Centroid Guided Joint Grouping","date":"2020-12-06","arxiv_id":"2012.03316","n_code_links":0,"syntology":null},{"paper":"/paper/food-classification-with-convolutional-neural","slug":"food-classification-with-convolutional-neural","title":"Food Classification with Convolutional Neural Networks and Multi-Class Linear Discernment Analysis","date":"2020-12-06","arxiv_id":"2012.03170","n_code_links":1,"syntology":null},{"paper":null,"slug":"multiple-networks-are-more-efficient-than-one","title":"Wisdom of Committees: An Overlooked Approach To Faster and More Accurate Models","date":"2020-12-03","arxiv_id":"2012.01988","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-once-for-all-budgeted-pruning-framework","title":"An Once-for-All Budgeted Pruning Framework for ConvNets Considering Input Resolution","date":"2020-12-02","arxiv_id":"2012.00996","n_code_links":0,"syntology":null},{"paper":"/paper/diverse-temporal-aggregation-and-depthwise","slug":"diverse-temporal-aggregation-and-depthwise","title":"Diverse Temporal Aggregation and Depthwise Spatiotemporal Factorization for Efficient Video Classification","date":"2020-12-01","arxiv_id":"2012.00317","n_code_links":1,"syntology":null},{"paper":"/paper/kafk-at-semeval-2020-task-8-extracting","slug":"kafk-at-semeval-2020-task-8-extracting","title":"KAFK at SemEval-2020 Task 8: Extracting Features from Pre-trained Neural Networks to Classify Internet Memes","date":"2020-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/model-rubiks-cube-twisting-resolution-depth","slug":"model-rubiks-cube-twisting-resolution-depth","title":"Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets","date":"2020-12-01","arxiv_id":null,"n_code_links":3,"syntology":null},{"paper":null,"slug":"semi-supervised-noisy-student-pre-training-on","title":"Semi-Supervised Noisy Student Pre-training on EfficientNet Architectures for Plant Pathology Classification","date":"2020-12-01","arxiv_id":"2012.00332","n_code_links":0,"syntology":null},{"paper":null,"slug":"depth-enhanced-feature-pyramid-network-for","title":"Depth-Enhanced Feature Pyramid Network for Occlusion-Aware Verification of Buildings from Oblique Images","date":"2020-11-26","arxiv_id":"2011.13226","n_code_links":0,"syntology":null},{"paper":null,"slug":"imagenet-pretrained-cnns-for-jpeg","title":"ImageNet Pretrained CNNs for JPEG Steganalysis","date":"2020-11-24","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"micronet-towards-image-recognition-with","title":"MicroNet: Towards Image Recognition with Extremely Low FLOPs","date":"2020-11-24","arxiv_id":"2011.12289","n_code_links":0,"syntology":null},{"paper":"/paper/automated-quality-assessment-of-hand-washing","slug":"automated-quality-assessment-of-hand-washing","title":"Automated Quality Assessment of Hand Washing Using Deep Learning","date":"2020-11-23","arxiv_id":"2011.11383","n_code_links":1,"syntology":null},{"paper":null,"slug":"deep-learning-model-trained-on-mobile-phone","title":"Deep learning model trained on mobile phone-acquired frozen section images effectively detects basal cell carcinoma","date":"2020-11-22","arxiv_id":"2011.11081","n_code_links":0,"syntology":null},{"paper":null,"slug":"efficient-embedding-network-for-3d-brain","title":"Efficient embedding network for 3D brain tumor segmentation","date":"2020-11-22","arxiv_id":"2011.11052","n_code_links":0,"syntology":null},{"paper":null,"slug":"fp-nas-fast-probabilistic-neural-architecture","title":"FP-NAS: Fast Probabilistic Neural Architecture Search","date":"2020-11-22","arxiv_id":"2011.10949","n_code_links":0,"syntology":null},{"paper":"/paper/optic-disc-cup-and-fovea-detection-from","slug":"optic-disc-cup-and-fovea-detection-from","title":"Optic Disc, Cup and Fovea Detection from Retinal Images Using U-Net++ with EfficientNet Encoder","date":"2020-11-20","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/an-efficient-and-scalable-deep-learning","slug":"an-efficient-and-scalable-deep-learning","title":"An Efficient and Scalable Deep Learning Approach for Road Damage Detection","date":"2020-11-18","arxiv_id":"2011.09577","n_code_links":2,"syntology":null},{"paper":null,"slug":"layer-wise-data-free-cnn-compression","title":"Layer-Wise Data-Free CNN Compression","date":"2020-11-18","arxiv_id":"2011.09058","n_code_links":0,"syntology":null},{"paper":"/paper/multigrid-in-channels-neural-network","slug":"multigrid-in-channels-neural-network","title":"MGIC: Multigrid-in-Channels Neural Network Architectures","date":"2020-11-17","arxiv_id":"2011.09128","n_code_links":1,"syntology":null},{"paper":"/paper/padim-a-patch-distribution-modeling-framework","slug":"padim-a-patch-distribution-modeling-framework","title":"PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization","date":"2020-11-17","arxiv_id":"2011.08785","n_code_links":26,"syntology":{"ran":7,"of":7,"n_ran_checked":2,"n_instrument":5,"unverified":0,"pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":null,"slug":"an-ensemble-based-approach-by-fine-tuning-the","title":"An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images","date":"2020-11-11","arxiv_id":"2011.05543","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-multi-plant-disease-diagnosis-method-using","title":"A Multi-Plant Disease Diagnosis Method using Convolutional Neural Network","date":"2020-11-10","arxiv_id":"2011.05151","n_code_links":0,"syntology":null},{"paper":null,"slug":"predictive-analysis-of-diabetic-retinopathy","title":"Predictive Analysis of Diabetic Retinopathy with Transfer Learning","date":"2020-11-08","arxiv_id":"2011.04052","n_code_links":0,"syntology":null},{"paper":"/paper/depthwise-multiception-convolution-for","slug":"depthwise-multiception-convolution-for","title":"Depthwise Multiception Convolution for Reducing Network Parameters without Sacrificing Accuracy","date":"2020-11-07","arxiv_id":"2011.03701","n_code_links":1,"syntology":null},{"paper":"/paper/deep-transfer-learning-for-automated","slug":"deep-transfer-learning-for-automated","title":"Deep Transfer Learning for Automated Diagnosis of Skin Lesions from Photographs","date":"2020-11-06","arxiv_id":"2011.04475","n_code_links":1,"syntology":null},{"paper":"/paper/deep-learning-framework-to-detect-face-masks","slug":"deep-learning-framework-to-detect-face-masks","title":"Deep Learning Framework to Detect Face Masks from Video Footage","date":"2020-11-04","arxiv_id":"2011.02371","n_code_links":1,"syntology":null},{"paper":null,"slug":"the-forchheim-image-database-for-camera","title":"The Forchheim Image Database for Camera Identification in the Wild","date":"2020-11-04","arxiv_id":"2011.02241","n_code_links":0,"syntology":null},{"paper":null,"slug":"cabinet-efficient-context-aggregation-network","title":"Real-time Semantic Segmentation with Context Aggregation Network","date":"2020-11-02","arxiv_id":"2011.00993","n_code_links":0,"syntology":null},{"paper":null,"slug":"83-imagenet-accuracy-in-one-hour","title":"Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour","date":"2020-10-30","arxiv_id":"2011.00071","n_code_links":0,"syntology":null}],"record_sha256":"bce6c262594480d4f6abe3c7c244385f1d5dbad2d480d20e5088acd543a7fc8c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}