{"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/7","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":7,"pages_in_order":14,"rows_per_page":100,"rows":[601,700],"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/6","next":"/method/depthwise-convolution/papers/8","papers":[{"paper":"/paper/western-mediterranean-wetlands-bird-species","slug":"western-mediterranean-wetlands-bird-species","title":"Western Mediterranean wetlands bird species classification: evaluating small-footprint deep learning approaches on a new annotated dataset","date":"2022-07-12","arxiv_id":"2207.05393","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-lexicon-and-depth-wise-separable","title":"A Lexicon and Depth-wise Separable Convolution Based Handwritten Text Recognition System","date":"2022-07-11","arxiv_id":"2207.04651","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-power-of-transfer-learning-in","title":"The Power of Transfer Learning in Agricultural Applications: AgriNet","date":"2022-07-08","arxiv_id":"2207.03881","n_code_links":0,"syntology":null},{"paper":null,"slug":"attention-round-for-post-training","title":"Attention Round for Post-Training Quantization","date":"2022-07-07","arxiv_id":"2207.03088","n_code_links":0,"syntology":null},{"paper":null,"slug":"real-time-egocentric-segmentation-for-video","title":"Real Time Egocentric Segmentation for Video-self Avatar in Mixed Reality","date":"2022-07-04","arxiv_id":"2207.01296","n_code_links":0,"syntology":null},{"paper":"/paper/covid-19-detection-using-transfer-learning-1","slug":"covid-19-detection-using-transfer-learning-1","title":"COVID-19 Detection Using Transfer Learning Approach from Computed Tomography Images","date":"2022-07-01","arxiv_id":"2207.00259","n_code_links":1,"syntology":null},{"paper":"/paper/network-amplification-with-efficient-macs","slug":"network-amplification-with-efficient-macs","title":"Network Amplification With Efficient MACs Allocation","date":"2022-07-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"the-importance-of-the-instantaneous-phase-for","title":"The Importance of the Instantaneous Phase for classification using Convolutional Neural Networks","date":"2022-07-01","arxiv_id":"2207.00672","n_code_links":0,"syntology":null},{"paper":"/paper/grouped-pointwise-convolutions-reduce","slug":"grouped-pointwise-convolutions-reduce","title":"Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks","date":"2022-06-30","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"pvt-cov19d-pyramid-vision-transformer-for","title":"PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosis","date":"2022-06-30","arxiv_id":"2206.15069","n_code_links":0,"syntology":null},{"paper":"/paper/cross-forgery-analysis-of-vision-transformers","slug":"cross-forgery-analysis-of-vision-transformers","title":"Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image Detection","date":"2022-06-28","arxiv_id":"2206.13829","n_code_links":2,"syntology":null},{"paper":"/paper/revbifpn-the-fully-reversible-bidirectional","slug":"revbifpn-the-fully-reversible-bidirectional","title":"RevBiFPN: The Fully Reversible Bidirectional Feature Pyramid Network","date":"2022-06-28","arxiv_id":"2206.14098","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":2,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["cerebrasresearch/revbifpn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"mushroom-image-recognition-and-distance","title":"Mushroom image recognition and distance generation based on attention-mechanism model and genetic information","date":"2022-06-27","arxiv_id":"2206.13383","n_code_links":0,"syntology":null},{"paper":"/paper/bag-of-tricks-for-long-tail-visual","slug":"bag-of-tricks-for-long-tail-visual","title":"Bag of Tricks for Long-Tail Visual Recognition of Animal Species in Camera-Trap Images","date":"2022-06-24","arxiv_id":"2206.12458","n_code_links":1,"syntology":null},{"paper":"/paper/design-and-analysis-of-novel-bit-flip-attacks","slug":"design-and-analysis-of-novel-bit-flip-attacks","title":"Design and Analysis of Novel Bit-flip Attacks and Defense Strategies for DNNs","date":"2022-06-24","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"single-morphing-attack-detection-using-1","title":"Single Morphing Attack Detection using Siamese Network and Few-shot Learning","date":"2022-06-22","arxiv_id":"2206.10969","n_code_links":0,"syntology":null},{"paper":null,"slug":"mestereo-du2cnn-a-novel-dual-channel-cnn-for","title":"MEStereo-Du2CNN: A Novel Dual Channel CNN for Learning Robust Depth Estimates from Multi-exposure Stereo Images for HDR 3D Applications","date":"2022-06-21","arxiv_id":"2206.10375","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-machine-learning-data-fusion-model-for-soil","title":"A Machine Learning Data Fusion Model for Soil Moisture Retrieval","date":"2022-06-20","arxiv_id":"2206.09649","n_code_links":0,"syntology":null},{"paper":null,"slug":"piecewise-linear-neural-networks-and-deep","title":"Piecewise Linear Neural Networks and Deep Learning","date":"2022-06-18","arxiv_id":"2206.09149","n_code_links":0,"syntology":null},{"paper":"/paper/sima-simple-softmax-free-attention-for-vision","slug":"sima-simple-softmax-free-attention-for-vision","title":"SimA: Simple Softmax-free Attention for Vision Transformers","date":"2022-06-17","arxiv_id":"2206.08898","n_code_links":1,"syntology":{"ran":2,"of":3,"n_ran_checked":2,"n_instrument":0,"unverified":1,"pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["ucdvision/sima"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"edge-inference-with-fully-differentiable","title":"Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks","date":"2022-06-15","arxiv_id":"2206.07741","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-adaptive-ensembling-for-image","slug":"efficient-adaptive-ensembling-for-image","title":"Efficient Adaptive Ensembling for Image Classification","date":"2022-06-15","arxiv_id":"2206.07394","n_code_links":0,"syntology":null},{"paper":null,"slug":"investigating-multi-feature-selection-and","title":"Investigating Multi-Feature Selection and Ensembling for Audio Classification","date":"2022-06-15","arxiv_id":"2206.07511","n_code_links":0,"syntology":null},{"paper":"/paper/video-based-frame-level-facial-analysis-of","slug":"video-based-frame-level-facial-analysis-of","title":"Video-Based Frame-Level Facial Analysis of Affective Behavior on Mobile Devices Using EfficientNets","date":"2022-06-10","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":"/paper/an-improved-one-millisecond-mobile-backbone","slug":"an-improved-one-millisecond-mobile-backbone","title":"MobileOne: An Improved One millisecond Mobile Backbone","date":"2022-06-08","arxiv_id":"2206.04040","n_code_links":10,"syntology":{"ran":4,"of":6,"n_ran_checked":3,"n_instrument":1,"unverified":2,"pointer_only":2,"phrase":"4 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; 1 where Syntology's instrument failed) · 2 unverified","official":{"repos":["apple/ml-mobileone","rwightman/pytorch-image-models"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"paper":null,"slug":"predictive-modeling-of-charge-levels-for","title":"Predictive Modeling of Charge Levels for Battery Electric Vehicles using CNN EfficientNet and IGTD Algorithm","date":"2022-06-07","arxiv_id":"2206.03612","n_code_links":0,"syntology":null},{"paper":null,"slug":"yolov5s-gtb-light-weighted-and-improved","title":"YOLOv5s-GTB: light-weighted and improved YOLOv5s for bridge crack detection","date":"2022-06-03","arxiv_id":"2206.01498","n_code_links":0,"syntology":null},{"paper":"/paper/efficientformer-vision-transformers-at","slug":"efficientformer-vision-transformers-at","title":"EfficientFormer: Vision Transformers at MobileNet Speed","date":"2022-06-02","arxiv_id":"2206.01191","n_code_links":13,"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":["rwightman/pytorch-image-models","snap-research/efficientformer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"gan-based-medical-image-small-region-forgery","title":"GAN-based Medical Image Small Region Forgery Detection via a Two-Stage Cascade Framework","date":"2022-05-30","arxiv_id":"2205.15170","n_code_links":0,"syntology":null},{"paper":"/paper/gator-customizable-channel-pruning-of-neural","slug":"gator-customizable-channel-pruning-of-neural","title":"Gator: Customizable Channel Pruning of Neural Networks with Gating","date":"2022-05-30","arxiv_id":"2205.15404","n_code_links":1,"syntology":null},{"paper":"/paper/efficientvit-enhanced-linear-attention-for","slug":"efficientvit-enhanced-linear-attention-for","title":"EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction","date":"2022-05-29","arxiv_id":"2205.14756","n_code_links":6,"syntology":{"ran":4,"of":4,"n_ran_checked":1,"n_instrument":3,"unverified":0,"pointer_only":0,"phrase":"4 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; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["mit-han-lab/efficientvit","rwightman/pytorch-image-models"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/v4d-voxel-for-4d-novel-view-synthesis","slug":"v4d-voxel-for-4d-novel-view-synthesis","title":"V4d: voxel for 4d novel view synthesis","date":"2022-05-28","arxiv_id":"2205.14332","n_code_links":1,"syntology":null},{"paper":"/paper/mocovit-mobile-convolutional-vision","slug":"mocovit-mobile-convolutional-vision","title":"MoCoViT: Mobile Convolutional Vision Transformer","date":"2022-05-25","arxiv_id":"2205.12635","n_code_links":1,"syntology":null},{"paper":"/paper/accurate-and-resource-efficient-lipreading","slug":"accurate-and-resource-efficient-lipreading","title":"Accurate and Resource-Efficient Lipreading with Efficientnetv2 and Transformers","date":"2022-05-23","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"paddy-doctor-a-visual-image-dataset-for-paddy","title":"Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking","date":"2022-05-23","arxiv_id":"2205.11108","n_code_links":0,"syntology":null},{"paper":"/paper/classification-of-quasars-galaxies-and-stars","slug":"classification-of-quasars-galaxies-and-stars","title":"Classification of Quasars, Galaxies, and Stars in the Mapping of the Universe Multi-modal Deep Learning","date":"2022-05-22","arxiv_id":"2205.10745","n_code_links":1,"syntology":null},{"paper":null,"slug":"visualizing-coatnet-predictions-for-aiding","title":"Visualizing CoAtNet Predictions for Aiding Melanoma Detection","date":"2022-05-21","arxiv_id":"2205.10515","n_code_links":0,"syntology":null},{"paper":null,"slug":"fused-deep-neural-network-based-transfer","title":"Fused Deep Neural Network based Transfer Learning in Occluded Face Classification and Person re-Identification","date":"2022-05-15","arxiv_id":"2205.07203","n_code_links":0,"syntology":null},{"paper":null,"slug":"novel-multicolumn-kernel-extreme-learning","title":"Novel Multicolumn Kernel Extreme Learning Machine for Food Detection via Optimal Features from CNN","date":"2022-05-15","arxiv_id":"2205.07348","n_code_links":0,"syntology":null},{"paper":"/paper/classification-of-astronomical-bodies-by","slug":"classification-of-astronomical-bodies-by","title":"Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural Networks","date":"2022-05-14","arxiv_id":"2205.07124","n_code_links":1,"syntology":null},{"paper":"/paper/efficient-deep-learning-methods-for","slug":"efficient-deep-learning-methods-for","title":"Efficient Deep Learning Methods for Identification of Defective Casting Products","date":"2022-05-14","arxiv_id":"2205.07118","n_code_links":1,"syntology":null},{"paper":null,"slug":"multi-modal-curb-detection-and-filtering","title":"Multi-modal curb detection and filtering","date":"2022-05-14","arxiv_id":"2205.07096","n_code_links":0,"syntology":null},{"paper":null,"slug":"revisiting-facial-key-point-detection-an","title":"Revisiting Facial Key Point Detection: An Efficient Approach Using Deep Neural Networks","date":"2022-05-14","arxiv_id":"2205.07121","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-effective-scheme-for-maize-disease","title":"An Effective Scheme for Maize Disease Recognition based on Deep Networks","date":"2022-05-09","arxiv_id":"2205.04234","n_code_links":0,"syntology":null},{"paper":"/paper/hierattn-effectively-learn-representations","slug":"hierattn-effectively-learn-representations","title":"Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention","date":"2022-05-09","arxiv_id":"2205.04326","n_code_links":2,"syntology":null},{"paper":"/paper/a-nas-neural-architecture-search-using","slug":"a-nas-neural-architecture-search-using","title":"Neural Architecture Search using Property Guided Synthesis","date":"2022-05-08","arxiv_id":"2205.03960","n_code_links":1,"syntology":null},{"paper":"/paper/rapq-rescuing-accuracy-for-power-of-two-low","slug":"rapq-rescuing-accuracy-for-power-of-two-low","title":"RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization","date":"2022-04-26","arxiv_id":"2204.12322","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":["billamihom/rapq"],"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":null,"slug":"unified-gcns-towards-connecting-gcns-with","title":"Unified GCNs: Towards Connecting GCNs with CNNs","date":"2022-04-26","arxiv_id":"2204.12300","n_code_links":0,"syntology":null},{"paper":"/paper/multimodal-hate-speech-detection-from-bengali","slug":"multimodal-hate-speech-detection-from-bengali","title":"Multimodal Hate Speech Detection from Bengali Memes and Texts","date":"2022-04-19","arxiv_id":"2204.10196","n_code_links":1,"syntology":null},{"paper":null,"slug":"application-of-transfer-learning-and-ensemble","title":"Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology","date":"2022-04-18","arxiv_id":"2204.08311","n_code_links":0,"syntology":null},{"paper":null,"slug":"insta-bnn-binary-neural-network-with-instance","title":"INSTA-BNN: Binary Neural Network with INSTAnce-aware Threshold","date":"2022-04-15","arxiv_id":"2204.07439","n_code_links":0,"syntology":null},{"paper":"/paper/dmcnet-diversified-model-combination-network","slug":"dmcnet-diversified-model-combination-network","title":"DMCNet: Diversified Model Combination Network for Understanding Engagement from Video Screengrabs","date":"2022-04-13","arxiv_id":"2204.06454","n_code_links":1,"syntology":null},{"paper":null,"slug":"spatiotemporal-estimation-of-tropomi-no2","title":"Spatiotemporal Estimation of TROPOMI NO2 Column with Depthwise Partial Convolutional Neural Network","date":"2022-04-12","arxiv_id":"2204.05917","n_code_links":0,"syntology":null},{"paper":"/paper/topformer-token-pyramid-transformer-for","slug":"topformer-token-pyramid-transformer-for","title":"TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation","date":"2022-04-12","arxiv_id":"2204.05525","n_code_links":3,"syntology":{"ran":2,"of":5,"n_ran_checked":2,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["hustvl/TopFormer"],"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":"searching-for-efficient-neural-architectures","title":"Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs","date":"2022-04-09","arxiv_id":"2204.14007","n_code_links":0,"syntology":null},{"paper":"/paper/uncertainty-informed-deep-learning-models","slug":"uncertainty-informed-deep-learning-models","title":"Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology","date":"2022-04-09","arxiv_id":"2204.04516","n_code_links":2,"syntology":{"ran":9,"of":11,"n_ran_checked":9,"n_instrument":0,"unverified":2,"pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","official":{"repos":["jamesdolezal/biscuit","jamesdolezal/slideflow"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":2,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"pneumonia-detection-in-chest-x-rays-using","title":"Pneumonia Detection in Chest X-Rays using Neural Networks","date":"2022-04-07","arxiv_id":"2204.03618","n_code_links":0,"syntology":null},{"paper":"/paper/efficientcellseg-efficient-volumetric-cell","slug":"efficientcellseg-efficient-volumetric-cell","title":"EfficientCellSeg: Efficient Volumetric Cell Segmentation Using Context Aware Pseudocoloring","date":"2022-04-06","arxiv_id":"2204.03014","n_code_links":1,"syntology":null},{"paper":"/paper/mixformer-mixing-features-across-windows-and","slug":"mixformer-mixing-features-across-windows-and","title":"MixFormer: Mixing Features across Windows and Dimensions","date":"2022-04-06","arxiv_id":"2204.02557","n_code_links":3,"syntology":{"ran":10,"of":14,"n_ran_checked":10,"n_instrument":0,"unverified":4,"pointer_only":0,"phrase":"10 ran (of which 3 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","official":{"repos":["PaddlePaddle/PaddleClas"],"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":"explainable-deep-learning-algorithm-for","title":"Explainable Deep Learning Algorithm for Distinguishing Incomplete Kawasaki Disease by Coronary Artery Lesions on Echocardiographic Imaging","date":"2022-04-05","arxiv_id":"2204.02403","n_code_links":0,"syntology":null},{"paper":null,"slug":"when-sparsity-meets-dynamic-convolution","title":"SD-Conv: Towards the Parameter-Efficiency of Dynamic Convolution","date":"2022-04-05","arxiv_id":"2204.02227","n_code_links":0,"syntology":null},{"paper":"/paper/rediscovery-of-the-effectiveness-of-standard","slug":"rediscovery-of-the-effectiveness-of-standard","title":"EResFD: Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection","date":"2022-04-04","arxiv_id":"2204.01209","n_code_links":1,"syntology":null},{"paper":"/paper/triplenet-a-low-computing-power-platform-of","slug":"triplenet-a-low-computing-power-platform-of","title":"Efficient Convolutional Neural Networks on Raspberry Pi for Image Classification","date":"2022-04-02","arxiv_id":"2204.00943","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["RuiyangJu/TripleNet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-temporal-oriented-broadcast-resnet-for","title":"A Temporal-oriented Broadcast ResNet for COVID-19 Detection","date":"2022-03-31","arxiv_id":"2203.17012","n_code_links":0,"syntology":null},{"paper":"/paper/sepvit-separable-vision-transformer","slug":"sepvit-separable-vision-transformer","title":"SepViT: Separable Vision Transformer","date":"2022-03-29","arxiv_id":"2203.15380","n_code_links":2,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"2 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; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["liwei109/sepvit"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]}}},{"paper":null,"slug":"multi-model-ensemble-learning-method-for","title":"Multi-model Ensemble Learning Method for Human Expression Recognition","date":"2022-03-28","arxiv_id":"2203.14466","n_code_links":0,"syntology":null},{"paper":"/paper/frame-level-prediction-of-facial-expressions","slug":"frame-level-prediction-of-facial-expressions","title":"Frame-level Prediction of Facial Expressions, Valence, Arousal and Action Units for Mobile Devices","date":"2022-03-25","arxiv_id":"2203.13436","n_code_links":2,"syntology":null},{"paper":null,"slug":"revisiting-multi-scale-feature-fusion-for","title":"Revisiting Multi-Scale Feature Fusion for Semantic Segmentation","date":"2022-03-23","arxiv_id":"2203.12683","n_code_links":0,"syntology":null},{"paper":null,"slug":"framehopper-selective-processing-of-video","title":"FrameHopper: Selective Processing of Video Frames in Detection-driven Real-Time Video Analytics","date":"2022-03-22","arxiv_id":"2203.11493","n_code_links":0,"syntology":null},{"paper":null,"slug":"enriching-and-characterizing-t-cell","title":"Enriching and Characterizing T-Cell Repertoires from 3' Barcoded Single-Cell Whole Transcriptome Amplification Products","date":"2022-03-21","arxiv_id":"2203.11266","n_code_links":0,"syntology":null},{"paper":"/paper/overcoming-oscillations-in-quantization-aware","slug":"overcoming-oscillations-in-quantization-aware","title":"Overcoming Oscillations in Quantization-Aware Training","date":"2022-03-21","arxiv_id":"2203.11086","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":["qualcomm-ai-research/oscillations-qat"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"paper":"/paper/defending-variational-autoencoders-from","slug":"defending-variational-autoencoders-from","title":"Alleviating Adversarial Attacks on Variational Autoencoders with MCMC","date":"2022-03-18","arxiv_id":"2203.09940","n_code_links":1,"syntology":null},{"paper":"/paper/hybridnets-end-to-end-perception-network-1","slug":"hybridnets-end-to-end-perception-network-1","title":"HybridNets: End-to-End Perception Network","date":"2022-03-17","arxiv_id":"2203.09035","n_code_links":3,"syntology":null},{"paper":"/paper/relational-self-supervised-learning","slug":"relational-self-supervised-learning","title":"Weak Augmentation Guided Relational Self-Supervised Learning","date":"2022-03-16","arxiv_id":"2203.08717","n_code_links":1,"syntology":{"ran":0,"of":2,"n_ran_checked":0,"n_instrument":0,"unverified":2,"pointer_only":2,"phrase":"0 ran · 2 unverified","official":{"repos":["mingkai-zheng/ReSSL"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"paper":null,"slug":"securing-the-classification-of-covid-19-in","title":"Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach","date":"2022-03-15","arxiv_id":"2203.07728","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-compilation-flow-for-the-generation-of-cnn","title":"A Compilation Flow for the Generation of CNN Inference Accelerators on FPGAs","date":"2022-03-08","arxiv_id":"2203.04015","n_code_links":0,"syntology":null},{"paper":null,"slug":"p2m-a-processing-in-pixel-in-memory-paradigm","title":"P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications","date":"2022-03-07","arxiv_id":"2203.04737","n_code_links":0,"syntology":null},{"paper":null,"slug":"depthwise-convolution-for-multi-agent","title":"Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation","date":"2022-03-06","arxiv_id":"2203.02896","n_code_links":0,"syntology":null},{"paper":null,"slug":"quantum-deep-learning-for-mutant-covid-19","title":"Quantum Deep Learning for Mutant COVID-19 Strain Prediction","date":"2022-03-04","arxiv_id":"2203.03556","n_code_links":0,"syntology":null},{"paper":"/paper/sfpn-synthetic-fpn-for-object-detection","slug":"sfpn-synthetic-fpn-for-object-detection","title":"SFPN: Synthetic FPN for Object Detection","date":"2022-03-04","arxiv_id":"2203.02445","n_code_links":1,"syntology":null},{"paper":null,"slug":"structured-pruning-is-all-you-need-for","title":"Structured Pruning is All You Need for Pruning CNNs at Initialization","date":"2022-03-04","arxiv_id":"2203.02549","n_code_links":0,"syntology":null},{"paper":"/paper/ad-corre-adaptive-correlation-based-loss-for","slug":"ad-corre-adaptive-correlation-based-loss-for","title":"Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild","date":"2022-03-03","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/polarity-sampling-quality-and-diversity","slug":"polarity-sampling-quality-and-diversity","title":"Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values","date":"2022-03-03","arxiv_id":"2203.01993","n_code_links":1,"syntology":null},{"paper":"/paper/advise-adaptive-feature-relevance-and-visual","slug":"advise-adaptive-feature-relevance-and-visual","title":"ADVISE: ADaptive Feature Relevance and VISual Explanations for Convolutional Neural Networks","date":"2022-03-02","arxiv_id":"2203.01289","n_code_links":1,"syntology":null},{"paper":"/paper/a-multi-scale-transformer-for-medical-image","slug":"a-multi-scale-transformer-for-medical-image","title":"A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark","date":"2022-02-28","arxiv_id":"2203.00131","n_code_links":2,"syntology":{"ran":3,"of":4,"n_ran_checked":3,"n_instrument":0,"unverified":1,"pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["yhygao/cbim-medical-image-segmentation"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-hardware-aware-system-for-accelerating-deep","title":"A Hardware-Aware System for Accelerating Deep Neural Network Optimization","date":"2022-02-25","arxiv_id":"2202.12954","n_code_links":0,"syntology":null},{"paper":"/paper/simplified-learning-of-cad-features","slug":"simplified-learning-of-cad-features","title":"Simplified Learning of CAD Features Leveraging a Deep Residual Autoencoder","date":"2022-02-21","arxiv_id":"2202.10099","n_code_links":1,"syntology":null},{"paper":null,"slug":"ams-adrn-at-semeval-2022-task-5-a-suitable","title":"AMS_ADRN at SemEval-2022 Task 5: A Suitable Image-text Multimodal Joint Modeling Method for Multi-task Misogyny Identification","date":"2022-02-18","arxiv_id":"2202.09099","n_code_links":0,"syntology":null},{"paper":null,"slug":"lg-lsq-learned-gradient-linear-symmetric","title":"LG-LSQ: Learned Gradient Linear Symmetric Quantization","date":"2022-02-18","arxiv_id":"2202.09009","n_code_links":0,"syntology":null},{"paper":null,"slug":"end-to-end-neuron-instance-segmentation-based","title":"A General Deep Learning framework for Neuron Instance Segmentation based on Efficient UNet and Morphological Post-processing","date":"2022-02-17","arxiv_id":"2202.08682","n_code_links":0,"syntology":null},{"paper":"/paper/dualconv-dual-convolutional-kernels-for","slug":"dualconv-dual-convolutional-kernels-for","title":"DualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks","date":"2022-02-15","arxiv_id":"2202.07481","n_code_links":1,"syntology":null},{"paper":null,"slug":"gaze-guided-class-activation-mapping","title":"Gaze-Guided Class Activation Mapping: Leveraging Human Attention for Network Attention in Chest X-rays Classification","date":"2022-02-15","arxiv_id":"2202.07107","n_code_links":0,"syntology":null},{"paper":null,"slug":"maximizing-audio-event-detection-model","title":"Maximizing Audio Event Detection Model Performance on Small Datasets Through Knowledge Transfer, Data Augmentation, And Pretraining: An Ablation Study","date":"2022-02-07","arxiv_id":"2202.03514","n_code_links":0,"syntology":null},{"paper":"/paper/learning-features-with-parameter-free-layers-1","slug":"learning-features-with-parameter-free-layers-1","title":"Learning Features with Parameter-Free Layers","date":"2022-02-06","arxiv_id":"2202.02777","n_code_links":1,"syntology":{"ran":0,"of":3,"n_ran_checked":0,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"0 ran · 3 unverified","official":{"repos":["naver-ai/pflayer"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":[]}}},{"paper":null,"slug":"algorithms-for-efficiently-learning-low-rank","title":"Nonlinear Initialization Methods for Low-Rank Neural Networks","date":"2022-02-02","arxiv_id":"2202.00834","n_code_links":0,"syntology":null},{"paper":"/paper/deep-kernelized-dense-geometric-matching","slug":"deep-kernelized-dense-geometric-matching","title":"DKM: Dense Kernelized Feature Matching for Geometry Estimation","date":"2022-02-01","arxiv_id":"2202.00667","n_code_links":1,"syntology":null},{"paper":null,"slug":"recognition-aware-learned-image-compression","title":"Recognition-Aware Learned Image Compression","date":"2022-02-01","arxiv_id":"2202.00198","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-of-fake-faces-in-videos","title":"Detection of fake faces in videos","date":"2022-01-28","arxiv_id":"2201.12051","n_code_links":0,"syntology":null},{"paper":"/paper/convolutional-xformers-for-vision","slug":"convolutional-xformers-for-vision","title":"Convolutional Xformers for Vision","date":"2022-01-25","arxiv_id":"2201.10271","n_code_links":1,"syntology":null},{"paper":null,"slug":"covid-19-detection-using-ct-image-based-on","title":"COVID-19 Detection Using CT Image Based On YOLOv5 Network","date":"2022-01-24","arxiv_id":"2201.09972","n_code_links":0,"syntology":null}],"record_sha256":"a157371bc580fd6d623c346ea739e6027a46f4fbe0ed95250549bdd295908542","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}