{"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":"/task/image-classification/papers/81","list_of":"/task/image-classification","task":"Image Classification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":81,"pages_in_order":105,"rows_per_page":100,"rows":[8001,8100],"of":10488,"counts":{"archive_papers_tagged":10488,"with_a_code_link":4702,"where_syntology_ran_a_sample":1392,"not_listed_spam_title":0,"listed":10488,"listed_where_code_ran":1392,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1164,"every_run_a_failure_of_syntologys_instrument":228,"listed_with_a_run_with_no_instrument_failure":1164,"listed_every_run_a_failure_of_syntologys_instrument":228,"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":"/task/image-classification","prev":"/task/image-classification/papers/80","next":"/task/image-classification/papers/82","papers":[{"url":null,"slug":"transfer-learning-for-automatic-brain-tumor","title":"Transfer learning for automatic brain tumor classification Using MRI Images.","date":"2021-03-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-knowledge-distillation-for","title":"Variational Knowledge Distillation for Disease Classification in Chest X-Rays","date":"2021-03-19","arxiv_id":"2103.10825","repositories_listed":0,"syntology":null},{"url":null,"slug":"stride-and-translation-invariance-in-cnns","title":"Stride and Translation Invariance in CNNs","date":"2021-03-18","arxiv_id":"2103.10097","repositories_listed":0,"syntology":null},{"url":null,"slug":"tppi-net-towards-efficient-and-practical","title":"TPPI-Net: Towards Efficient and Practical Hyperspectral Image Classification","date":"2021-03-18","arxiv_id":"2103.10084","repositories_listed":0,"syntology":null},{"url":null,"slug":"hamil-hierarchical-aggregation-based-multi","title":"HAMIL: Hierarchical Aggregation-Based Multi-Instance Learning for Microscopy Image Classification","date":"2021-03-17","arxiv_id":"2103.09764","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-zero-shot-image-classification","title":"Large-Scale Zero-Shot Image Classification from Rich and Diverse Textual Descriptions","date":"2021-03-17","arxiv_id":"2103.09669","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantitative-effectiveness-assessment-and","title":"Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation","date":"2021-03-17","arxiv_id":"2103.09716","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-yolo-defense-human-detection","title":"Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches","date":"2021-03-16","arxiv_id":"2103.08860","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-deep-learning-using-volunteer","title":"Distributed Deep Learning Using Volunteer Computing-Like Paradigm","date":"2021-03-16","arxiv_id":"2103.08894","repositories_listed":0,"syntology":null},{"url":null,"slug":"learned-gradient-compression-for-distributed","title":"Learned Gradient Compression for Distributed Deep Learning","date":"2021-03-16","arxiv_id":"2103.08870","repositories_listed":0,"syntology":null},{"url":null,"slug":"evolving-parametrized-loss-for-image","title":"Evolving parametrized Loss for Image Classification Learning on Small Datasets","date":"2021-03-15","arxiv_id":"2103.08249","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-distribute-data-across-tasks-for-meta","title":"How to distribute data across tasks for meta-learning?","date":"2021-03-15","arxiv_id":"2103.08463","repositories_listed":0,"syntology":null},{"url":null,"slug":"sampling-free-variational-inference-for","title":"Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise","date":"2021-03-15","arxiv_id":"2103.08497","repositories_listed":0,"syntology":null},{"url":null,"slug":"siamese-network-features-for-endoscopy-image","title":"Distance Metric-Based Learning with Interpolated Latent Features for Location Classification in Endoscopy Image and Video","date":"2021-03-15","arxiv_id":"2103.08504","repositories_listed":0,"syntology":null},{"url":null,"slug":"crossoverscheduler-overlapping-multiple","title":"CrossoverScheduler: Overlapping Multiple Distributed Training Applications in a Crossover Manner","date":"2021-03-14","arxiv_id":"2103.07974","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-sparse-artificial-neural-networks","title":"Efficient Sparse Artificial Neural Networks","date":"2021-03-13","arxiv_id":"2103.07674","repositories_listed":0,"syntology":null},{"url":null,"slug":"interleaving-learning-with-application-to","title":"Interleaving Learning, with Application to Neural Architecture Search","date":"2021-03-12","arxiv_id":"2103.07018","repositories_listed":0,"syntology":null},{"url":null,"slug":"learnable-companding-quantization-for","title":"Learnable Companding Quantization for Accurate Low-bit Neural Networks","date":"2021-03-12","arxiv_id":"2103.07156","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-random-network-for-fine-grained","title":"Sequential Random Network for Fine-grained Image Classification","date":"2021-03-12","arxiv_id":"2103.07230","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-guided-model-generalization-to","title":"Uncertainty-guided Model Generalization to Unseen Domains","date":"2021-03-12","arxiv_id":"2103.07531","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-copy-blend-augmentation-for-low","title":"Evaluating COPY-BLEND Augmentation for Low Level Vision Tasks","date":"2021-03-10","arxiv_id":"2103.05889","repositories_listed":0,"syntology":null},{"url":null,"slug":"transmed-transformers-advance-multi-modal","title":"TransMed: Transformers Advance Multi-modal Medical Image Classification","date":"2021-03-10","arxiv_id":"2103.05940","repositories_listed":0,"syntology":null},{"url":null,"slug":"why-flatness-correlates-with-generalization","title":"Why flatness does and does not correlate with generalization for deep neural networks","date":"2021-03-10","arxiv_id":"2103.06219","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-global-adversarial-robustness","title":"Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN","date":"2021-03-08","arxiv_id":"2103.04513","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-model-performance-estimation-via-1","title":"Efficient Model Performance Estimation via Feature Histories","date":"2021-03-07","arxiv_id":"2103.04450","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrospective-approximation-for-smooth","title":"A Retrospective Approximation Approach for Smooth Stochastic Optimization","date":"2021-03-07","arxiv_id":"2103.04392","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-optimized-image-compression-for-1","title":"End-to-end optimized image compression for multiple machine tasks","date":"2021-03-06","arxiv_id":"2103.04178","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-model-biases-in-the-absence-of","title":"Measuring Model Biases in the Absence of Ground Truth","date":"2021-03-05","arxiv_id":"2103.03417","repositories_listed":0,"syntology":null},{"url":null,"slug":"scrib-set-classifier-with-class-specific-risk","title":"SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models","date":"2021-03-05","arxiv_id":"2103.03945","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-robust-training-for-graph","title":"Unified Robust Training for Graph NeuralNetworks against Label Noise","date":"2021-03-05","arxiv_id":"2103.03414","repositories_listed":0,"syntology":null},{"url":null,"slug":"hard-label-manifolds-unexpected-advantages-of-1","title":"Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples","date":"2021-03-04","arxiv_id":"2103.03325","repositories_listed":0,"syntology":null},{"url":null,"slug":"qair-practical-query-efficient-black-box","title":"QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval","date":"2021-03-04","arxiv_id":"2103.02927","repositories_listed":0,"syntology":null},{"url":null,"slug":"redundant-information-neural-estimation","title":"Redundant Information Neural Estimation","date":"2021-03-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-for-image","title":"Unsupervised Domain Adaptation for Image Classification via Structure-Conditioned Adversarial Learning","date":"2021-03-04","arxiv_id":"2103.02808","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-alternative-practice-of-tropical","title":"An Alternative Practice of Tropical Convolution to Traditional Convolutional Neural Networks","date":"2021-03-03","arxiv_id":"2103.02096","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-invariant-representations-across","title":"Learning Invariant Representations across Domains and Tasks","date":"2021-03-03","arxiv_id":"2103.05114","repositories_listed":0,"syntology":null},{"url":null,"slug":"vanishing-twin-gan-how-training-a-weak","title":"Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification","date":"2021-03-03","arxiv_id":"2103.02496","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-structurally-regularized-convolutional","title":"A Structurally Regularized Convolutional Neural Network for Image Classification using Wavelet-based SubBand Decomposition","date":"2021-03-02","arxiv_id":"2103.01823","repositories_listed":0,"syntology":null},{"url":null,"slug":"all-at-once-network-quantization-via","title":"Improved Techniques for Quantizing Deep Networks with Adaptive Bit-Widths","date":"2021-03-02","arxiv_id":"2103.01435","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-methods-generalizing-max-and","title":"Comparison of Methods Generalizing Max- and Average-Pooling","date":"2021-03-02","arxiv_id":"2103.01746","repositories_listed":0,"syntology":null},{"url":null,"slug":"demystifying-batch-normalization-in-relu","title":"Demystifying Batch Normalization in ReLU Networks: Equivalent Convex Optimization Models and Implicit Regularization","date":"2021-03-02","arxiv_id":"2103.01499","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-programming-is-immune-to-adversarial","title":"Brain Programming is Immune to Adversarial Attacks: Towards Accurate and Robust Image Classification using Symbolic Learning","date":"2021-03-01","arxiv_id":"2103.01359","repositories_listed":0,"syntology":null},{"url":null,"slug":"diversifying-sample-generation-for-accurate","title":"Diversifying Sample Generation for Accurate Data-Free Quantization","date":"2021-03-01","arxiv_id":"2103.01049","repositories_listed":0,"syntology":null},{"url":null,"slug":"maximal-function-pooling-with-applications","title":"Maximal function pooling with applications","date":"2021-03-01","arxiv_id":"2103.01292","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiclass-burn-wound-image-classification","title":"Multiclass Burn Wound Image Classification Using Deep Convolutional Neural Networks","date":"2021-03-01","arxiv_id":"2103.01361","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-medical-image-classification-with","title":"Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation","date":"2021-02-28","arxiv_id":"2103.00528","repositories_listed":0,"syntology":null},{"url":null,"slug":"virus-mnist-a-benchmark-malware-dataset","title":"Virus-MNIST: A Benchmark Malware Dataset","date":"2021-02-28","arxiv_id":"2103.00602","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-learning-for-covid-19-image","title":"Semi-supervised Learning for COVID-19 Image Classification via ResNet","date":"2021-02-27","arxiv_id":"2103.06140","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-laplace-for-bayesian-neural","title":"Variational Laplace for Bayesian neural networks","date":"2021-02-27","arxiv_id":"2103.00222","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-universal-model-for-cross-modality-mapping","title":"A Universal Model for Cross Modality Mapping by Relational Reasoning","date":"2021-02-26","arxiv_id":"2102.13360","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-knowledge-overlay-to-visual-feature","title":"Class Knowledge Overlay to Visual Feature Learning for Zero-Shot Image Classification","date":"2021-02-26","arxiv_id":"2102.13322","repositories_listed":0,"syntology":null},{"url":null,"slug":"highly-efficient-representation-and-active","title":"Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification","date":"2021-02-25","arxiv_id":"2103.05109","repositories_listed":0,"syntology":null},{"url":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","repositories_listed":0,"syntology":null},{"url":null,"slug":"web-table-classification-based-on-visual","title":"Web Table Classification based on Visual Features","date":"2021-02-25","arxiv_id":"2103.05110","repositories_listed":0,"syntology":null},{"url":null,"slug":"arguments-for-the-unsuitability-of","title":"Arguments for the Unsuitability of Convolutional Neural Networks for Non--Local Tasks","date":"2021-02-23","arxiv_id":"2102.11944","repositories_listed":0,"syntology":null},{"url":null,"slug":"probabilistic-spatial-analysis-in","title":"Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density Maps","date":"2021-02-23","arxiv_id":"2102.11865","repositories_listed":0,"syntology":null},{"url":null,"slug":"csit-free-federated-edge-learning-via","title":"CSIT-Free Model Aggregation for Federated Edge Learning via Reconfigurable Intelligent Surface","date":"2021-02-22","arxiv_id":"2102.10749","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainers-in-the-wild-making-surrogate","title":"Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception","date":"2021-02-22","arxiv_id":"2102.10951","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hierarchical-conditional-random-field-based","title":"A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification","date":"2021-02-21","arxiv_id":"2102.10499","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-spiking-neural-network-and","title":"Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification","date":"2021-02-21","arxiv_id":"2102.10592","repositories_listed":0,"syntology":null},{"url":null,"slug":"emds-5-environmental-microorganism-image","title":"EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks","date":"2021-02-20","arxiv_id":"2102.10370","repositories_listed":0,"syntology":null},{"url":null,"slug":"neural-sampling-machine-with-stochastic","title":"Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference","date":"2021-02-20","arxiv_id":"2102.10477","repositories_listed":0,"syntology":null},{"url":null,"slug":"fortify-machine-learning-production-systems","title":"Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks","date":"2021-02-19","arxiv_id":"2102.09695","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-classification-using-cnn-for-traffic","title":"Image Classification using CNN for Traffic Signs in Pakistan","date":"2021-02-19","arxiv_id":"2102.10130","repositories_listed":0,"syntology":null},{"url":null,"slug":"frugalmct-efficient-online-ml-api-selection","title":"Efficient Online ML API Selection for Multi-Label Classification Tasks","date":"2021-02-18","arxiv_id":"2102.09127","repositories_listed":0,"syntology":null},{"url":"/paper/centroid-transformers-learning-to-abstract","slug":"centroid-transformers-learning-to-abstract","title":"Centroid Transformers: Learning to Abstract with Attention","date":"2021-02-17","arxiv_id":"2102.08606","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-domain-free-domain-generalization-with","title":"Robust Domain-Free Domain Generalization with Class-aware Alignment","date":"2021-02-17","arxiv_id":"2102.08897","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-weighting-scheme-for-automatic-time","title":"Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation","date":"2021-02-16","arxiv_id":"2102.08310","repositories_listed":0,"syntology":null},{"url":null,"slug":"does-deep-machine-vision-have-just-noticeable","title":"Just Noticeable Difference for Deep Machine Vision","date":"2021-02-16","arxiv_id":"2102.08168","repositories_listed":0,"syntology":null},{"url":null,"slug":"just-noticeable-difference-for-machine","title":"Just Noticeable Difference for Machine Perception and Generation of Regularized Adversarial Images with Minimal Perturbation","date":"2021-02-16","arxiv_id":"2102.08079","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-stacked-denoising-autoencoders-for","title":"Training Stacked Denoising Autoencoders for Representation Learning","date":"2021-02-16","arxiv_id":"2102.08012","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-misinformation-from-website","title":"Identifying Misinformation from Website Screenshots","date":"2021-02-15","arxiv_id":"2102.07849","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-image-quality-assessment-by","title":"Learning image quality assessment by reinforcing task amenable data selection","date":"2021-02-15","arxiv_id":"2102.07615","repositories_listed":0,"syntology":null},{"url":null,"slug":"naturalizing-neuromorphic-vision-event","title":"Naturalizing Neuromorphic Vision Event Streams Using GANs","date":"2021-02-14","arxiv_id":"2102.07243","repositories_listed":0,"syntology":null},{"url":null,"slug":"perceptually-constrained-adversarial-attacks","title":"Perceptually Constrained Adversarial Attacks","date":"2021-02-14","arxiv_id":"2102.07140","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-class-generative-adversarial-nets-for","title":"Multi-class Generative Adversarial Nets for Semi-supervised Image Classification","date":"2021-02-13","arxiv_id":"2102.06944","repositories_listed":0,"syntology":null},{"url":"/paper/a-large-batch-optimizer-reality-check","slug":"a-large-batch-optimizer-reality-check","title":"A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes","date":"2021-02-12","arxiv_id":"2102.06356","repositories_listed":0,"syntology":null},{"url":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","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-localized-adversarial-examples-a","title":"Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis","date":"2021-02-10","arxiv_id":"2102.05241","repositories_listed":0,"syntology":null},{"url":null,"slug":"demystifying-code-summarization-models","title":"WheaCha: A Method for Explaining the Predictions of Models of Code","date":"2021-02-09","arxiv_id":"2102.04625","repositories_listed":0,"syntology":null},{"url":null,"slug":"distribution-adaptive-int8-quantization-for","title":"Distribution Adaptive INT8 Quantization for Training CNNs","date":"2021-02-09","arxiv_id":"2102.04782","repositories_listed":0,"syntology":null},{"url":null,"slug":"flow-mixup-classifying-multi-labeled-medical","title":"Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels","date":"2021-02-09","arxiv_id":"2102.08148","repositories_listed":0,"syntology":null},{"url":null,"slug":"soccer-event-detection-using-deep-learning","title":"Soccer Event Detection Using Deep Learning","date":"2021-02-08","arxiv_id":"2102.04331","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-video-classification-with","title":"Privacy-Preserving Video Classification with Convolutional Neural Networks","date":"2021-02-06","arxiv_id":"2102.03513","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-implicit-biases-of-stochastic-gradient","title":"Weight Rescaling: Effective and Robust Regularization for Deep Neural Networks with Batch Normalization","date":"2021-02-06","arxiv_id":"2102.03497","repositories_listed":0,"syntology":null},{"url":null,"slug":"achieving-explainability-for-plant-disease","title":"Achieving Explainability for Plant Disease Classification with Disentangled Variational Autoencoders","date":"2021-02-05","arxiv_id":"2102.03082","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspherical-embedding-for-novel-class","title":"Hyperspherical embedding for novel class classification","date":"2021-02-05","arxiv_id":"2102.03243","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-reproducibility-of-neural-network-1","title":"On the Reproducibility of Neural Network Predictions","date":"2021-02-05","arxiv_id":"2102.03349","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-supervised-segmentation-network-for","title":"A Supervised Segmentation Network for Hyperspectral Image Classification","date":"2021-02-04","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-reinforcement-learning-based-image-1","title":"Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 images","date":"2021-02-04","arxiv_id":"2102.02895","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-cnns-for-large-scale-species","title":"Deep CNNs for large scale species classification","date":"2021-02-03","arxiv_id":"2102.01863","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-instance-learning-by-utilizing","title":"Multi-Instance Learning by Utilizing Structural Relationship among Instances","date":"2021-02-03","arxiv_id":"2102.01889","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-approach-for-classification-and","title":"A Novel Approach for Classification and Forecasting of Time Series in Particle Accelerators","date":"2021-02-01","arxiv_id":"2102.00786","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-image-classification-with-multi","title":"Few-shot Image Classification with Multi-Facet Prototypes","date":"2021-02-01","arxiv_id":"2102.00801","repositories_listed":0,"syntology":null},{"url":"/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","repositories_listed":0,"syntology":null},{"url":null,"slug":"spectral-roll-off-points-estimating-useful","title":"Spectral Roll-off Points Variations: Exploring Useful Information in Feature Maps by Its Variations","date":"2021-01-31","arxiv_id":"2102.00369","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-over-wireless-device-to","title":"Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis","date":"2021-01-29","arxiv_id":"2101.12704","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-deep-radial-basis-function-data","title":"The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection","date":"2021-01-29","arxiv_id":"2101.12632","repositories_listed":0,"syntology":null},{"url":null,"slug":"compas-representation-learning-with","title":"CORL: Compositional Representation Learning for Few-Shot Classification","date":"2021-01-28","arxiv_id":"2101.11878","repositories_listed":0,"syntology":null},{"url":null,"slug":"information-contraction-in-noisy-binary","title":"Information contraction in noisy binary neural networks and its implications","date":"2021-01-28","arxiv_id":"2101.11750","repositories_listed":0,"syntology":null}],"record_sha256":"516a1a448dba65e5f3c7dd991f8dd4076e3688bb4cb9b8414ecc3640a04abf4d","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}