{"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/average-pooling/papers/21","list_of":"/method/average-pooling","method":"Average Pooling","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":21,"pages_in_order":52,"rows_per_page":100,"rows":[2001,2100],"of":5125,"counts":{"archive_papers_tagged":5125,"with_a_code_link":2243,"where_syntology_ran_a_sample":586,"not_listed_spam_title":0,"listed":5125,"listed_where_code_ran":586,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":489,"every_run_a_failure_of_syntologys_instrument":97,"listed_with_a_run_with_no_instrument_failure":489,"listed_every_run_a_failure_of_syntologys_instrument":97,"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/average-pooling","prev":"/method/average-pooling/papers/20","next":"/method/average-pooling/papers/22","papers":[{"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":"sqsgd-locally-private-and-communication","title":"sqSGD: Locally Private and Communication Efficient Federated Learning","date":"2022-06-21","arxiv_id":"2206.10565","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":"deep-learning-based-defect-classification-and","title":"Deep Learning-Based Defect Classification and Detection in SEM Images","date":"2022-06-20","arxiv_id":"2206.13505","n_code_links":0,"syntology":null},{"paper":"/paper/msanet-multi-similarity-and-attention-1","slug":"msanet-multi-similarity-and-attention-1","title":"MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot Segmentation","date":"2022-06-20","arxiv_id":"2206.09667","n_code_links":1,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","official":{"repos":["AIVResearch/MSANet"],"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":["unlocated"]}}},{"paper":"/paper/visualizing-and-understanding-self-supervised","slug":"visualizing-and-understanding-self-supervised","title":"Visualizing and Understanding Contrastive Learning","date":"2022-06-20","arxiv_id":"2206.09753","n_code_links":1,"syntology":null},{"paper":null,"slug":"wolonet-wave-outlooker-for-efficient-and-high","title":"WOLONet: Wave Outlooker for Efficient and High Fidelity Speech Synthesis","date":"2022-06-20","arxiv_id":"2206.09920","n_code_links":0,"syntology":null},{"paper":"/paper/ctooth-a-fully-annotated-3d-dataset-and","slug":"ctooth-a-fully-annotated-3d-dataset-and","title":"CTooth: A Fully Annotated 3D Dataset and Benchmark for Tooth Volume Segmentation on Cone Beam Computed Tomography Images","date":"2022-06-17","arxiv_id":"2206.08778","n_code_links":1,"syntology":null},{"paper":null,"slug":"evaluation-of-contrastive-learning-with","title":"Evaluation of Contrastive Learning with Various Code Representations for Code Clone Detection","date":"2022-06-17","arxiv_id":"2206.08726","n_code_links":0,"syntology":null},{"paper":null,"slug":"multi-classification-of-brain-tumor-images","title":"Multi-Classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network","date":"2022-06-17","arxiv_id":"2206.08543","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":"backbones-review-feature-extraction-networks","title":"Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches","date":"2022-06-16","arxiv_id":"2206.08016","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-detection-of-rice-disease-in-images","title":"Automatic Detection of Rice Disease in Images of Various Leaf Sizes","date":"2022-06-15","arxiv_id":"2206.07344","n_code_links":0,"syntology":null},{"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":"evaluating-object-detector-ensembles-for","title":"Evaluating object detector ensembles for improving the robustness of artifact detection in endoscopic video streams","date":"2022-06-15","arxiv_id":"2206.07580","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":null,"slug":"read-aggregating-reconstruction-error-into","title":"READ: Aggregating Reconstruction Error into Out-of-distribution Detection","date":"2022-06-15","arxiv_id":"2206.07459","n_code_links":0,"syntology":null},{"paper":null,"slug":"subsurface-depths-structure-maps","title":"Subsurface Depths Structure Maps Reconstruction with Generative Adversarial Networks","date":"2022-06-15","arxiv_id":"2206.07388","n_code_links":0,"syntology":null},{"paper":"/paper/towards-ml-methods-for-biodiversity-a-novel","slug":"towards-ml-methods-for-biodiversity-a-novel","title":"Towards ML Methods for Biodiversity: A Novel Wild Bee Dataset and Evaluations of XAI Methods for ML-Assisted Rare Species Annotations","date":"2022-06-15","arxiv_id":"2206.07497","n_code_links":1,"syntology":null},{"paper":null,"slug":"fluorescence-angiography-classification-in","title":"Fluorescence angiography classification in colorectal surgery -- A preliminary report","date":"2022-06-13","arxiv_id":"2206.05935","n_code_links":0,"syntology":null},{"paper":"/paper/making-sense-of-dependence-efficient-black","slug":"making-sense-of-dependence-efficient-black","title":"Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure","date":"2022-06-13","arxiv_id":"2206.06219","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":["paulnovello/hsic-attribution-method"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":"/paper/mmmna-net-for-overall-survival-time","slug":"mmmna-net-for-overall-survival-time","title":"MMMNA-Net for Overall Survival Time Prediction of Brain Tumor Patients","date":"2022-06-13","arxiv_id":"2206.06267","n_code_links":1,"syntology":null},{"paper":"/paper/towards-alternative-techniques-for-improving","slug":"towards-alternative-techniques-for-improving","title":"Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations","date":"2022-06-13","arxiv_id":"2206.06496","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-directed-evolution-method-for","title":"A Directed-Evolution Method for Sparsification and Compression of Neural Networks with Application to Object Identification and Segmentation and considerations of optimal quantization using small number of bits","date":"2022-06-12","arxiv_id":"2206.05859","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-models-for-automated","title":"Deep Learning Models for Automated Classification of Dog Emotional States from Facial Expressions","date":"2022-06-11","arxiv_id":"2206.05619","n_code_links":0,"syntology":null},{"paper":"/paper/symbolic-image-detection-using-scene-and","slug":"symbolic-image-detection-using-scene-and","title":"Symbolic image detection using scene and knowledge graphs","date":"2022-06-10","arxiv_id":"2206.04863","n_code_links":1,"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/cross-modal-local-shortest-path-and-global","slug":"cross-modal-local-shortest-path-and-global","title":"Cross-modal Local Shortest Path and Global Enhancement for Visible-Thermal Person Re-Identification","date":"2022-06-09","arxiv_id":"2206.04401","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-neural-network-for-blind-visual-quality","title":"Deep Neural Network for Blind Visual Quality Assessment of 4K Content","date":"2022-06-09","arxiv_id":"2206.04363","n_code_links":0,"syntology":null},{"paper":"/paper/gasp-gated-attention-for-saliency-prediction-1","slug":"gasp-gated-attention-for-saliency-prediction-1","title":"GASP: Gated Attention For Saliency Prediction","date":"2022-06-09","arxiv_id":"2206.04590","n_code_links":1,"syntology":null},{"paper":null,"slug":"sdq-stochastic-differentiable-quantization","title":"SDQ: Stochastic Differentiable Quantization with Mixed Precision","date":"2022-06-09","arxiv_id":"2206.04459","n_code_links":0,"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":"/paper/ensembling-framework-for-texture-extraction","slug":"ensembling-framework-for-texture-extraction","title":"Texture Extraction Methods Based Ensembling Framework for Improved Classification","date":"2022-06-08","arxiv_id":"2206.04158","n_code_links":1,"syntology":null},{"paper":"/paper/wavelet-regularization-benefits-adversarial","slug":"wavelet-regularization-benefits-adversarial","title":"Wavelet Regularization Benefits Adversarial Training","date":"2022-06-08","arxiv_id":"2206.03727","n_code_links":1,"syntology":null},{"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":"/paper/tadml-a-fast-temporal-action-detection-with","slug":"tadml-a-fast-temporal-action-detection-with","title":"TadML: A fast temporal action detection with Mechanics-MLP","date":"2022-06-07","arxiv_id":"2206.02997","n_code_links":1,"syntology":null},{"paper":"/paper/sports-re-id-improving-re-identification-of","slug":"sports-re-id-improving-re-identification-of","title":"Sports Re-ID: Improving Re-Identification Of Players In Broadcast Videos Of Team Sports","date":"2022-06-06","arxiv_id":"2206.02373","n_code_links":1,"syntology":null},{"paper":"/paper/why-do-cnns-learn-consistent-representations","slug":"why-do-cnns-learn-consistent-representations","title":"What do CNNs Learn in the First Layer and Why? A Linear Systems Perspective","date":"2022-06-06","arxiv_id":"2206.02454","n_code_links":1,"syntology":null},{"paper":null,"slug":"searching-similarity-measure-for-binarized","title":"Searching Similarity Measure for Binarized Neural Networks","date":"2022-06-05","arxiv_id":"2206.03325","n_code_links":0,"syntology":null},{"paper":null,"slug":"cainnflow-convolutional-block-attention","title":"CAINNFlow: Convolutional block Attention modules and Invertible Neural Networks Flow for anomaly detection and localization tasks","date":"2022-06-04","arxiv_id":"2206.01992","n_code_links":0,"syntology":null},{"paper":"/paper/contrastive-learning-unifies-t-sne-and-umap","slug":"contrastive-learning-unifies-t-sne-and-umap","title":"From $t$-SNE to UMAP with contrastive learning","date":"2022-06-03","arxiv_id":"2206.01816","n_code_links":3,"syntology":{"ran":6,"of":7,"n_ran_checked":6,"n_instrument":0,"unverified":1,"pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","official":{"repos":["hci-unihd/cl-tsne-umap","berenslab/contrastive-ne"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"eaanet-efficient-attention-augmented","title":"EAANet: Efficient Attention Augmented Convolutional Networks","date":"2022-06-03","arxiv_id":"2206.01821","n_code_links":0,"syntology":null},{"paper":null,"slug":"on-the-duality-between-contrastive-and-non","title":"On the duality between contrastive and non-contrastive self-supervised learning","date":"2022-06-03","arxiv_id":"2206.02574","n_code_links":0,"syntology":null},{"paper":null,"slug":"radar-guided-dynamic-visual-attention-for","title":"Radar Guided Dynamic Visual Attention for Resource-Efficient RGB Object Detection","date":"2022-06-03","arxiv_id":"2206.01772","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":"equivariant-reinforcement-learning-for","title":"Equivariant Reinforcement Learning for Quadrotor UAV","date":"2022-06-02","arxiv_id":"2206.01233","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-generalized-supervised-contrastive-learning","title":"Generalized Supervised Contrastive Learning","date":"2022-06-01","arxiv_id":"2206.00384","n_code_links":0,"syntology":null},{"paper":null,"slug":"landslide4sense-reference-benchmark-data-and","title":"Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection","date":"2022-06-01","arxiv_id":"2206.00515","n_code_links":0,"syntology":null},{"paper":"/paper/needle-in-a-haystack-fast-benchmarking-image","slug":"needle-in-a-haystack-fast-benchmarking-image","title":"Needle In A Haystack, Fast: Benchmarking Image Perceptual Similarity Metrics At Scale","date":"2022-06-01","arxiv_id":"2206.00282","n_code_links":1,"syntology":null},{"paper":null,"slug":"realistic-deep-learning-may-not-fit-benignly","title":"Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models","date":"2022-06-01","arxiv_id":"2206.00501","n_code_links":0,"syntology":null},{"paper":null,"slug":"research-on-smoking-behavior-detection-system","title":"Research on Smoking Behavior Detection System Based on Deep Learning","date":"2022-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"rotate-the-relu-to-implicitly-sparsify-deep","title":"Rotate the ReLU to implicitly sparsify deep networks","date":"2022-06-01","arxiv_id":"2206.00488","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-reward-poisoning-attacks-on-online","slug":"efficient-reward-poisoning-attacks-on-online","title":"Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning","date":"2022-05-30","arxiv_id":"2205.14842","n_code_links":1,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["yinglunxu/reward_poisoning_attack_drl"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"exposing-fine-grained-adversarial","title":"Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models","date":"2022-05-30","arxiv_id":"2205.14851","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":null,"slug":"do-residual-neural-networks-discretize-neural","title":"Do Residual Neural Networks discretize Neural Ordinary Differential Equations?","date":"2022-05-29","arxiv_id":"2205.14612","n_code_links":0,"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/baddet-backdoor-attacks-on-object-detection","slug":"baddet-backdoor-attacks-on-object-detection","title":"BadDet: Backdoor Attacks on Object Detection","date":"2022-05-28","arxiv_id":"2205.14497","n_code_links":1,"syntology":null},{"paper":"/paper/mdmlp-image-classification-from-scratch-on","slug":"mdmlp-image-classification-from-scratch-on","title":"MDMLP: Image Classification from Scratch on Small Datasets with MLP","date":"2022-05-28","arxiv_id":"2205.14477","n_code_links":2,"syntology":null},{"paper":"/paper/wavemix-lite-a-resource-efficient-neural","slug":"wavemix-lite-a-resource-efficient-neural","title":"WaveMix: A Resource-efficient Neural Network for Image Analysis","date":"2022-05-28","arxiv_id":"2205.14375","n_code_links":1,"syntology":null},{"paper":null,"slug":"standalone-neural-odes-with-sensitivity","title":"Standalone Neural ODEs with Sensitivity Analysis","date":"2022-05-27","arxiv_id":"2205.13933","n_code_links":0,"syntology":null},{"paper":"/paper/membership-inference-attack-using-self","slug":"membership-inference-attack-using-self","title":"Membership Inference Attack Using Self Influence Functions","date":"2022-05-26","arxiv_id":"2205.13680","n_code_links":1,"syntology":null},{"paper":"/paper/impartial-games-a-challenge-for-reinforcement","slug":"impartial-games-a-challenge-for-reinforcement","title":"Impartial Games: A Challenge for Reinforcement Learning","date":"2022-05-25","arxiv_id":"2205.12787","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":null,"slug":"deep-geometric-moment","title":"Improving Shape Awareness and Interpretability in Deep Networks Using Geometric Moments","date":"2022-05-24","arxiv_id":"2205.11722","n_code_links":0,"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":"contrastive-and-non-contrastive-self","title":"Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods","date":"2022-05-23","arxiv_id":"2205.11508","n_code_links":0,"syntology":null},{"paper":"/paper/discriminative-feature-learning-through","slug":"discriminative-feature-learning-through","title":"Discriminative Feature Learning through Feature Distance Loss","date":"2022-05-23","arxiv_id":"2205.11606","n_code_links":1,"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/training-efficient-cnns-tweaking-the-nuts-and","slug":"training-efficient-cnns-tweaking-the-nuts-and","title":"Training Efficient CNNS: Tweaking the Nuts and Bolts of Neural Networks for Lighter, Faster and Robust Models","date":"2022-05-23","arxiv_id":"2205.12050","n_code_links":1,"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":"investigating-classification-learning-curves","title":"Investigating classification learning curves for automatically generated and labelled plant images","date":"2022-05-22","arxiv_id":"2205.10955","n_code_links":0,"syntology":null},{"paper":"/paper/test-time-robust-personalization-for","slug":"test-time-robust-personalization-for","title":"Test-Time Robust Personalization for Federated Learning","date":"2022-05-22","arxiv_id":"2205.10920","n_code_links":1,"syntology":{"ran":11,"of":14,"n_ran_checked":11,"n_instrument":0,"unverified":3,"pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","official":{"repos":["lins-lab/fedthe"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":"/paper/scalable-and-efficient-training-of-large","slug":"scalable-and-efficient-training-of-large","title":"Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy","date":"2022-05-21","arxiv_id":"2205.10683","n_code_links":1,"syntology":{"ran":1,"of":4,"n_ran_checked":0,"n_instrument":1,"unverified":3,"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) · 3 unverified","official":{"repos":["woodyx218/private_vision"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"a-demographic-attribute-guided-approach-to","title":"A Demographic Attribute Guided Approach to Age Estimation","date":"2022-05-20","arxiv_id":"2205.10254","n_code_links":0,"syntology":null},{"paper":"/paper/kernel-normalized-convolutional-networks","slug":"kernel-normalized-convolutional-networks","title":"Kernel Normalized Convolutional Networks","date":"2022-05-20","arxiv_id":"2205.10089","n_code_links":1,"syntology":null},{"paper":null,"slug":"semi-supervised-learning-for-image","title":"Semi-Supervised Learning for Image Classification using Compact Networks in the BioMedical Context","date":"2022-05-19","arxiv_id":"2205.09678","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatial-autoregressive-coding-for-graph","title":"Spatial Autoregressive Coding for Graph Neural Recommendation","date":"2022-05-19","arxiv_id":"2205.09489","n_code_links":0,"syntology":null},{"paper":null,"slug":"application-of-graph-based-features-in","title":"Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer","date":"2022-05-17","arxiv_id":"2205.08467","n_code_links":0,"syntology":null},{"paper":null,"slug":"unraveling-attention-via-convex-duality","title":"Unraveling Attention via Convex Duality: Analysis and Interpretations of Vision Transformers","date":"2022-05-17","arxiv_id":"2205.08078","n_code_links":0,"syntology":null},{"paper":"/paper/diffusion-models-for-adversarial-purification","slug":"diffusion-models-for-adversarial-purification","title":"Diffusion Models for Adversarial Purification","date":"2022-05-16","arxiv_id":"2205.07460","n_code_links":2,"syntology":{"ran":16,"of":23,"n_ran_checked":12,"n_instrument":4,"unverified":7,"pointer_only":9,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 3 honoured, 0 violated, 9 with no contract checked; 4 where Syntology's instrument failed) · 7 unverified","official":{"repos":["NVlabs/DiffPure"],"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":["found_in_text","official","unlocated"]}}},{"paper":"/paper/pillarnet-high-performance-pillar-based-3d","slug":"pillarnet-high-performance-pillar-based-3d","title":"PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detection","date":"2022-05-16","arxiv_id":"2205.07403","n_code_links":1,"syntology":null},{"paper":null,"slug":"analyzing-lottery-ticket-hypothesis-from-pac","title":"Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective","date":"2022-05-15","arxiv_id":"2205.07320","n_code_links":0,"syntology":null},{"paper":"/paper/cmelgan-an-efficient-conditional-generative","slug":"cmelgan-an-efficient-conditional-generative","title":"cMelGAN: An Efficient Conditional Generative Model Based on Mel Spectrograms","date":"2022-05-15","arxiv_id":"2205.07319","n_code_links":1,"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":"infrared-invisible-clothing-hiding-from","title":"Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World","date":"2022-05-12","arxiv_id":"2205.05909","n_code_links":0,"syntology":null},{"paper":"/paper/identical-image-retrieval-using-deep-learning","slug":"identical-image-retrieval-using-deep-learning","title":"Identical Image Retrieval using Deep Learning","date":"2022-05-10","arxiv_id":"2205.04883","n_code_links":1,"syntology":null},{"paper":null,"slug":"object-detection-in-indian-food-platters","title":"Object Detection in Indian Food Platters using Transfer Learning with YOLOv4","date":"2022-05-10","arxiv_id":"2205.04841","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-frequency-attention-to-make-adversarial","title":"Using Frequency Attention to Make Adversarial Patch Powerful Against Person Detector","date":"2022-05-10","arxiv_id":"2205.04638","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/object-detection-with-spiking-neural-networks","slug":"object-detection-with-spiking-neural-networks","title":"Object Detection with Spiking Neural Networks on Automotive Event Data","date":"2022-05-09","arxiv_id":"2205.04339","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","official":{"repos":["loiccordone/object-detection-with-spiking-neural-networks"],"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"]}}}],"record_sha256":"2971949ef2191b79db67106bae89fa0a2c583e63a6c9f4a29cfa39f63a84c5c5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}