{"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/73","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":73,"pages_in_order":105,"rows_per_page":100,"rows":[7201,7300],"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/72","next":"/task/image-classification/papers/74","papers":[{"url":null,"slug":"when-accuracy-meets-privacy-two-stage","title":"A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study","date":"2022-03-24","arxiv_id":"2203.12803","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-fully-distributed-federated","title":"Efficient Fully Distributed Federated Learning with Adaptive Local Links","date":"2022-03-23","arxiv_id":"2203.12281","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-test-time-unsupervised-deep","title":"Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices","date":"2022-03-21","arxiv_id":"2203.11295","repositories_listed":0,"syntology":null},{"url":null,"slug":"computer-vision-and-machine-learning-for","title":"Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward","date":"2022-03-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"image-classification-on-accelerated-neural","title":"Image Classification on Accelerated Neural Networks","date":"2022-03-21","arxiv_id":"2203.11081","repositories_listed":0,"syntology":null},{"url":null,"slug":"mtbf-33-a-multi-temporal-building-footprint","title":"MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900-2015)","date":"2022-03-21","arxiv_id":"2203.11078","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-self-supervised-gaze-estimation","title":"Towards Self-Supervised Gaze Estimation","date":"2022-03-21","arxiv_id":"2203.10974","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-quantised-neural-networks-with-ste","title":"Training Quantised Neural Networks with STE Variants: the Additive Noise Annealing Algorithm","date":"2022-03-21","arxiv_id":"2203.11323","repositories_listed":0,"syntology":null},{"url":null,"slug":"crispnet-color-rendition-isp-net","title":"CRISPnet: Color Rendition ISP Net","date":"2022-03-20","arxiv_id":"2203.10562","repositories_listed":0,"syntology":null},{"url":null,"slug":"over-parameterization-a-necessary-condition","title":"Over-parameterization: A Necessary Condition for Models that Extrapolate","date":"2022-03-20","arxiv_id":"2203.10447","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-transformer-with-convolutions","title":"Vision Transformer with Convolutions Architecture Search","date":"2022-03-20","arxiv_id":"2203.10435","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-generalization-extrapolation","title":"Deep Learning Generalization, Extrapolation, and Over-parameterization","date":"2022-03-19","arxiv_id":"2203.10366","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-few-shot-learning-via-implanting","title":"Incremental Few-Shot Learning via Implanting and Compressing","date":"2022-03-19","arxiv_id":"2203.10297","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-interactive-explanatory-ai-system-for","title":"An Interactive Explanatory AI System for Industrial Quality Control","date":"2022-03-17","arxiv_id":"2203.09181","repositories_listed":0,"syntology":null},{"url":null,"slug":"confidence-dimension-for-deep-learning-based","title":"Confidence Dimension for Deep Learning based on Hoeffding Inequality and Relative Evaluation","date":"2022-03-17","arxiv_id":"2203.09082","repositories_listed":0,"syntology":null},{"url":null,"slug":"transframer-arbitrary-frame-prediction-with","title":"Transframer: Arbitrary Frame Prediction with Generative Models","date":"2022-03-17","arxiv_id":"2203.09494","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-continual-learning-framework-for-adaptive","title":"A Continual Learning Framework for Adaptive Defect Classification and Inspection","date":"2022-03-16","arxiv_id":"2203.08796","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-quantum-density-matrix-in","title":"A New Quantum CNN Model for Image Classification","date":"2022-03-16","arxiv_id":"2203.11155","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-it-all-a-cluster-game-exploring-out-of","title":"Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space","date":"2022-03-16","arxiv_id":"2203.08549","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-learning-of-nas-for-few-shot-learning-in","title":"Meta-Learning of NAS for Few-shot Learning in Medical Image Applications","date":"2022-03-16","arxiv_id":"2203.08951","repositories_listed":0,"syntology":null},{"url":null,"slug":"2-speed-network-ensemble-for-efficient","title":"2-speed network ensemble for efficient classification of incremental land-use/land-cover satellite image chips","date":"2022-03-15","arxiv_id":"2203.08267","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-but-not-robust-comparing-the","title":"Generalized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness","date":"2022-03-15","arxiv_id":"2203.07653","repositories_listed":0,"syntology":null},{"url":null,"slug":"inscon-instance-consistency-feature","title":"InsCon:Instance Consistency Feature Representation via Self-Supervised Learning","date":"2022-03-15","arxiv_id":"2203.07688","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-ordinal-regression-forest-for-medical","title":"Meta Ordinal Regression Forest for Medical Image Classification with Ordinal Labels","date":"2022-03-15","arxiv_id":"2203.07725","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-network-doesn-t-rule-them-all-moving","title":"One Network Doesn't Rule Them All: Moving Beyond Handcrafted Architectures in Self-Supervised Learning","date":"2022-03-15","arxiv_id":"2203.08130","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-understanding-deep-learning-with-the","title":"Towards understanding deep learning with the natural clustering prior","date":"2022-03-15","arxiv_id":"2203.08174","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-view-prediction-exploring-contrastive","title":"Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification","date":"2022-03-14","arxiv_id":"2203.07000","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-calibration-of-pre-trained-language","title":"On the Calibration of Pre-trained Language Models using Mixup Guided by Area Under the Margin and Saliency","date":"2022-03-14","arxiv_id":"2203.07559","repositories_listed":0,"syntology":null},{"url":null,"slug":"univip-a-unified-framework-for-self","title":"UniVIP: A Unified Framework for Self-Supervised Visual Pre-training","date":"2022-03-14","arxiv_id":"2203.06965","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-from-attacks-attacking-variational","title":"Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification","date":"2022-03-11","arxiv_id":"2203.07027","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-classification-of-medical","title":"GSDA: Generative Adversarial Network-based Semi-Supervised Data Augmentation for Ultrasound Image Classification","date":"2022-03-11","arxiv_id":"2203.06184","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-subspace-clustering-for-concept","title":"Sparse Subspace Clustering for Concept Discovery (SSCCD)","date":"2022-03-11","arxiv_id":"2203.06043","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-consistency-loss-for-training-multi","title":"Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotations","date":"2022-03-11","arxiv_id":"2203.06127","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-self-semi-supervised-learning-for-few","title":"Active Self-Semi-Supervised Learning for Few Labeled Samples","date":"2022-03-09","arxiv_id":"2203.04560","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiscale-convolutional-transformer-with","title":"Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classification","date":"2022-03-09","arxiv_id":"2203.04771","repositories_listed":0,"syntology":null},{"url":null,"slug":"renyi-fair-information-bottleneck-for-image","title":"Renyi Fair Information Bottleneck for Image Classification","date":"2022-03-09","arxiv_id":"2203.04950","repositories_listed":0,"syntology":null},{"url":null,"slug":"uni4eye-unified-2d-and-3d-self-supervised-pre","title":"Uni4Eye: Unified 2D and 3D Self-supervised Pre-training via Masked Image Modeling Transformer for Ophthalmic Image Classification","date":"2022-03-09","arxiv_id":"2203.04614","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-group-transformer-a-general-vision","title":"Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention","date":"2022-03-08","arxiv_id":"2203.03937","repositories_listed":0,"syntology":null},{"url":null,"slug":"art-attack-black-box-adversarial-attack-via","title":"Art-Attack: Black-Box Adversarial Attack via Evolutionary Art","date":"2022-03-07","arxiv_id":"2203.04405","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-convnets-on-tiny-devices-via-nested","title":"Dynamic ConvNets on Tiny Devices via Nested Sparsity","date":"2022-03-07","arxiv_id":"2203.03324","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-for-image","title":"Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations","date":"2022-03-07","arxiv_id":"2203.03457","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-interpretability-methods-and","title":"Fidelity of Interpretability Methods and Perturbation Artifacts in Neural Networks","date":"2022-03-06","arxiv_id":"2203.02928","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-backdoors-with-global-average-pooling","title":"Dynamic Backdoors with Global Average Pooling","date":"2022-03-04","arxiv_id":"2203.02079","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairprune-achieving-fairness-through-pruning","title":"FairPrune: Achieving Fairness Through Pruning for Dermatological Disease Diagnosis","date":"2022-03-04","arxiv_id":"2203.02110","repositories_listed":0,"syntology":null},{"url":null,"slug":"adafamily-a-family-of-adam-like-adaptive","title":"AdaFamily: A family of Adam-like adaptive gradient methods","date":"2022-03-03","arxiv_id":"2203.01603","repositories_listed":0,"syntology":null},{"url":"/paper/aggregated-pyramid-vision-transformer-split","slug":"aggregated-pyramid-vision-transformer-split","title":"Aggregated Pyramid Vision Transformer: Split-transform-merge Strategy for Image Recognition without Convolutions","date":"2022-03-02","arxiv_id":"2203.00960","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuous-time-meta-learning-with-forward-1","title":"Continuous-Time Meta-Learning with Forward Mode Differentiation","date":"2022-03-02","arxiv_id":"2203.01443","repositories_listed":0,"syntology":null},{"url":null,"slug":"miashield-defending-membership-inference","title":"MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members","date":"2022-03-02","arxiv_id":"2203.00915","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-feature-encoding-for-gnns-on-road","title":"Visual Feature Encoding for GNNs on Road Networks","date":"2022-03-02","arxiv_id":"2203.01187","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-deep-learning-for-image","title":"Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Survey","date":"2022-03-01","arxiv_id":"2203.00190","repositories_listed":0,"syntology":null},{"url":null,"slug":"amortized-proximal-optimization","title":"Amortized Proximal Optimization","date":"2022-02-28","arxiv_id":"2203.00089","repositories_listed":0,"syntology":null},{"url":null,"slug":"esw-edge-weights-ensemble-stochastic","title":"ESW Edge-Weights : Ensemble Stochastic Watershed Edge-Weights for Hyperspectral Image Classification","date":"2022-02-28","arxiv_id":"2202.13502","repositories_listed":0,"syntology":null},{"url":null,"slug":"synergistic-network-learning-and-label","title":"Synergistic Network Learning and Label Correction for Noise-robust Image Classification","date":"2022-02-27","arxiv_id":"2202.13472","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-neural-architecture-exploration","title":"Accelerating Neural Architecture Exploration Across Modalities Using Genetic Algorithms","date":"2022-02-25","arxiv_id":"2202.12934","repositories_listed":0,"syntology":null},{"url":null,"slug":"goal-oriented-communication-for-edge-learning","title":"Goal-Oriented Communication for Edge Learning based on the Information Bottleneck","date":"2022-02-25","arxiv_id":"2202.12639","repositories_listed":0,"syntology":null},{"url":null,"slug":"monogenic-wavelet-scattering-network-for","title":"Monogenic Wavelet Scattering Network for Texture Image Classification","date":"2022-02-25","arxiv_id":"2202.12491","repositories_listed":0,"syntology":null},{"url":null,"slug":"rrl-regional-rotation-layer-in-convolutional","title":"RRL:Regional Rotation Layer in Convolutional Neural Networks","date":"2022-02-25","arxiv_id":"2202.12509","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-enables-zero-approximation-error","title":"Attention Enables Zero Approximation Error","date":"2022-02-24","arxiv_id":"2202.12166","repositories_listed":0,"syntology":null},{"url":null,"slug":"autocl-a-visual-interactive-system-for","title":"AutoCl : A Visual Interactive System for Automatic Deep Learning Classifier Recommendation Based on Models Performance","date":"2022-02-24","arxiv_id":"2202.11928","repositories_listed":0,"syntology":null},{"url":null,"slug":"new-benchmark-for-household-garbage-image","title":"New Benchmark for Household Garbage Image Recognition","date":"2022-02-24","arxiv_id":"2202.11878","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-training-a-survey","title":"Self-Training: A Survey","date":"2022-02-24","arxiv_id":"2202.12040","repositories_listed":0,"syntology":null},{"url":null,"slug":"sutd-prcm-dataset-and-neural-architecture","title":"SUTD-PRCM Dataset and Neural Architecture Search Approach for Complex Metasurface Design","date":"2022-02-24","arxiv_id":"2203.00002","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-reproducibility-and-explainable","title":"Deep Learning Reproducibility and Explainable AI (XAI)","date":"2022-02-23","arxiv_id":"2202.11452","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixed-block-neural-architecture-search-for","title":"Mixed-Block Neural Architecture Search for Medical Image Segmentation","date":"2022-02-23","arxiv_id":"2202.11401","repositories_listed":0,"syntology":null},{"url":null,"slug":"cut-and-continuous-paste-towards-real-time","title":"Cut and Continuous Paste towards Real-time Deep Fall Detection","date":"2022-02-22","arxiv_id":"2202.10687","repositories_listed":0,"syntology":null},{"url":"/paper/retrieval-augmented-classification-for-long","slug":"retrieval-augmented-classification-for-long","title":"Retrieval Augmented Classification for Long-Tail Visual Recognition","date":"2022-02-22","arxiv_id":"2202.11233","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-the-zigzag-flattening-for-image","title":"Rethinking the Zigzag Flattening for Image Reading","date":"2022-02-21","arxiv_id":"2202.10240","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-note-on-the-implicit-bias-towards-minimal","title":"On the Implicit Bias Towards Minimal Depth of Deep Neural Networks","date":"2022-02-18","arxiv_id":"2202.09028","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentially-private-federated-learning-via-1","title":"Differentially Private Federated Learning via Inexact ADMM with Multiple Local Updates","date":"2022-02-18","arxiv_id":"2202.09409","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-data-detection-using","title":"Out of Distribution Data Detection Using Dropout Bayesian Neural Networks","date":"2022-02-18","arxiv_id":"2202.08985","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-pareto-frontier-for-performance","title":"Rethinking Pareto Frontier for Performance Evaluation of Deep Neural Networks","date":"2022-02-18","arxiv_id":"2202.09275","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-object-comprehension-a-framework-for","title":"Dynamic Object Comprehension: A Framework For Evaluating Artificial Visual Perception","date":"2022-02-17","arxiv_id":"2202.08490","repositories_listed":0,"syntology":null},{"url":null,"slug":"ebhi-a-new-enteroscope-biopsy","title":"EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation","date":"2022-02-17","arxiv_id":"2202.08552","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizable-information-theoretic-causal","title":"Generalizable Information Theoretic Causal Representation","date":"2022-02-17","arxiv_id":"2202.08388","repositories_listed":0,"syntology":null},{"url":null,"slug":"mlp-asr-sequence-length-agnostic-all-mlp","title":"MLP-ASR: Sequence-length agnostic all-MLP architectures for speech recognition","date":"2022-02-17","arxiv_id":"2202.08456","repositories_listed":0,"syntology":null},{"url":null,"slug":"two-stage-architectural-fine-tuning-with","title":"Two-stage architectural fine-tuning with neural architecture search using early-stopping in image classification","date":"2022-02-17","arxiv_id":"2202.08604","repositories_listed":0,"syntology":null},{"url":null,"slug":"applying-adversarial-networks-to-increase-the","title":"Applying adversarial networks to increase the data efficiency and reliability of Self-Driving Cars","date":"2022-02-16","arxiv_id":"2202.07815","repositories_listed":0,"syntology":null},{"url":null,"slug":"cross-modal-common-representation-learning","title":"Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition","date":"2022-02-16","arxiv_id":"2202.07901","repositories_listed":0,"syntology":null},{"url":null,"slug":"measuring-unintended-memorisation-of-unique","title":"Measuring Unintended Memorisation of Unique Private Features in Neural Networks","date":"2022-02-16","arxiv_id":"2202.08099","repositories_listed":0,"syntology":null},{"url":"/paper/meta-knowledge-distillation","slug":"meta-knowledge-distillation","title":"Meta Knowledge Distillation","date":"2022-02-16","arxiv_id":"2202.07940","repositories_listed":0,"syntology":null},{"url":null,"slug":"unified-smoke-and-fire-detection-in-an","title":"Unified smoke and fire detection in an evolutionary framework with self-supervised progressive data augment","date":"2022-02-16","arxiv_id":"2202.07954","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-precortical-module-for-robust-cnns-to-light-1","title":"A precortical module for robust CNNs to light variations","date":"2022-02-15","arxiv_id":"2202.07432","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-theory-of-pac-learnability-under","title":"A Theory of PAC Learnability under Transformation Invariances","date":"2022-02-15","arxiv_id":"2202.07552","repositories_listed":0,"syntology":null},{"url":null,"slug":"discriminability-enforcing-loss-to-improve","title":"Discriminability-enforcing loss to improve representation learning","date":"2022-02-14","arxiv_id":"2202.07073","repositories_listed":0,"syntology":null},{"url":null,"slug":"fuzzy-pooling-1","title":"Fuzzy Pooling","date":"2022-02-12","arxiv_id":"2202.08372","repositories_listed":0,"syntology":null},{"url":null,"slug":"exemplar-free-online-continual-learning","title":"Exemplar-free Online Continual Learning","date":"2022-02-11","arxiv_id":"2202.05491","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-level-augmentation-to-improve","title":"Feature-level augmentation to improve robustness of deep neural networks to affine transformations","date":"2022-02-10","arxiv_id":"2202.05152","repositories_listed":0,"syntology":null},{"url":null,"slug":"spherical-transformer","title":"Spherical Transformer","date":"2022-02-10","arxiv_id":"2202.04942","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-greedy-core-set-configurations-for-1","title":"Improving greedy core-set configurations for active learning with uncertainty-scaled distances","date":"2022-02-09","arxiv_id":"2202.04251","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-consistency-for-weakly-supervised","title":"Data Consistency for Weakly Supervised Learning","date":"2022-02-08","arxiv_id":"2202.03987","repositories_listed":0,"syntology":null},{"url":null,"slug":"if-a-human-can-see-it-so-should-your-system","title":"If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components","date":"2022-02-08","arxiv_id":"2202.03930","repositories_listed":0,"syntology":null},{"url":null,"slug":"corrupted-image-modeling-for-self-supervised","title":"Corrupted Image Modeling for Self-Supervised Visual Pre-Training","date":"2022-02-07","arxiv_id":"2202.03382","repositories_listed":0,"syntology":null},{"url":null,"slug":"simple-control-baselines-for-evaluating","title":"Simple Control Baselines for Evaluating Transfer Learning","date":"2022-02-07","arxiv_id":"2202.03365","repositories_listed":0,"syntology":null},{"url":null,"slug":"decision-boundaries-and-convex-hulls-in-the","title":"Decision boundaries and convex hulls in the feature space that deep learning functions learn from images","date":"2022-02-05","arxiv_id":"2202.04052","repositories_listed":0,"syntology":null},{"url":null,"slug":"choosing-an-appropriate-platform-and-workflow","title":"Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence","date":"2022-02-04","arxiv_id":"2202.02283","repositories_listed":0,"syntology":null},{"url":null,"slug":"best-practices-and-scoring-system-on","title":"Best Practices and Scoring System on Reviewing A.I. based Medical Imaging Papers: Part 1 Classification","date":"2022-02-03","arxiv_id":"2202.01863","repositories_listed":0,"syntology":null},{"url":null,"slug":"forml-learning-to-reweight-data-for-fairness","title":"FORML: Learning to Reweight Data for Fairness","date":"2022-02-03","arxiv_id":"2202.01719","repositories_listed":0,"syntology":null},{"url":null,"slug":"access-control-of-object-detection-models","title":"Access Control of Object Detection Models Using Encrypted Feature Maps","date":"2022-02-01","arxiv_id":"2202.00265","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-skin-cancer-images-using","title":"Classification of Skin Cancer Images using Convolutional Neural Networks","date":"2022-02-01","arxiv_id":"2202.00678","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-active-learning-f-al-an-efficient","title":"Federated Active Learning (F-AL): an Efficient Annotation Strategy for Federated Learning","date":"2022-02-01","arxiv_id":"2202.00195","repositories_listed":0,"syntology":null}],"record_sha256":"fe73916c2b05f6738cbfc8b322e95621029d83c8d0bc00369f3df3f9fead7710","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}