{"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/87","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":87,"pages_in_order":105,"rows_per_page":100,"rows":[8601,8700],"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/86","next":"/task/image-classification/papers/88","papers":[{"url":null,"slug":"extracurricular-learning-knowledge-transfer","title":"Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution","date":"2020-06-30","arxiv_id":"2007.00051","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-ensemble-deep-learning-for","title":"Active Ensemble Deep Learning for Polarimetric Synthetic Aperture Radar Image Classification","date":"2020-06-29","arxiv_id":"2006.15771","repositories_listed":0,"syntology":null},{"url":null,"slug":"cluster-based-partitioning-of-convolutional","title":"Conditional Classification: A Solution for Computational Energy Reduction","date":"2020-06-29","arxiv_id":"2006.15799","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-3d-convolutional-neural-networks","title":"Explainable 3D Convolutional Neural Networks by Learning Temporal Transformations","date":"2020-06-29","arxiv_id":"2006.15983","repositories_listed":0,"syntology":null},{"url":null,"slug":"compositional-convolutional-neural-networks-a-2","title":"Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion","date":"2020-06-28","arxiv_id":"2006.15538","repositories_listed":0,"syntology":null},{"url":null,"slug":"frequency-learning-for-image-classification","title":"Frequency learning for image classification","date":"2020-06-28","arxiv_id":"2006.15476","repositories_listed":0,"syntology":null},{"url":"/paper/performing-image-classification-for-10","slug":"performing-image-classification-for-10","title":"Performing Image Classification for 10 Different Monkey Species using CNN","date":"2020-06-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-evoked-potential-guided-deep-learning","title":"An Evoked Potential-Guided Deep Learning Brain Representation For Visual Classification","date":"2020-06-27","arxiv_id":"2006.15357","repositories_listed":0,"syntology":null},{"url":null,"slug":"pclnet-a-practical-way-for-unsupervised-deep","title":"Unsupervised Deep Representation Learning and Few-Shot Classification of PolSAR Images","date":"2020-06-27","arxiv_id":"2006.15351","repositories_listed":0,"syntology":null},{"url":null,"slug":"4s-dt-self-supervised-super-sample","title":"4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection","date":"2020-06-26","arxiv_id":"2007.11450","repositories_listed":0,"syntology":null},{"url":null,"slug":"4s-dt-self-supervised-super-sample-1","title":"​4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection","date":"2020-06-26","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"e2gc-energy-efficient-group-convolution-in","title":"E2GC: Energy-efficient Group Convolution in Deep Neural Networks","date":"2020-06-26","arxiv_id":"2006.15100","repositories_listed":0,"syntology":null},{"url":null,"slug":"end-to-end-training-of-deep-kernel-map","title":"End-to-end training of deep kernel map networks for image classification","date":"2020-06-26","arxiv_id":"2006.15088","repositories_listed":0,"syntology":null},{"url":null,"slug":"layerwise-learning-for-quantum-neural","title":"Layerwise learning for quantum neural networks","date":"2020-06-26","arxiv_id":"2006.14904","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-diverse-latent-representations-for","title":"Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks","date":"2020-06-26","arxiv_id":"2006.15127","repositories_listed":0,"syntology":null},{"url":null,"slug":"not-all-failure-modes-are-created-equal","title":"Not all Failure Modes are Created Equal: Training Deep Neural Networks for Explicable (Mis)Classification","date":"2020-06-26","arxiv_id":"2006.14841","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-subset-selection","title":"Set Based Stochastic Subsampling","date":"2020-06-25","arxiv_id":"2006.14222","repositories_listed":0,"syntology":null},{"url":null,"slug":"target-consistency-for-domain-adaptation-when","title":"Target Consistency for Domain Adaptation: when Robustness meets Transferability","date":"2020-06-25","arxiv_id":"2006.14263","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-deep-models-for-cardiac","title":"Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction","date":"2020-06-24","arxiv_id":"2006.13811","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-interclass-relations-for-image","title":"Learning Interclass Relations for Image Classification","date":"2020-06-24","arxiv_id":"2006.13491","repositories_listed":0,"syntology":null},{"url":null,"slug":"ramanujan-bipartite-graph-products-for","title":"Ramanujan Bipartite Graph Products for Efficient Block Sparse Neural Networks","date":"2020-06-24","arxiv_id":"2006.13486","repositories_listed":0,"syntology":null},{"url":null,"slug":"time-for-a-background-check-uncovering-the","title":"Time for a Background Check! Uncovering the impact of Background Features on Deep Neural Networks","date":"2020-06-24","arxiv_id":"2006.14077","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-hoc-calibration-of-neural-networks","title":"Post-hoc Calibration of Neural Networks by g-Layers","date":"2020-06-23","arxiv_id":"2006.12807","repositories_listed":0,"syntology":null},{"url":"/paper/dont-wait-just-weight-improving-unsupervised","slug":"dont-wait-just-weight-improving-unsupervised","title":"Don’t Wait, Just Weight: Improving Unsupervised Representations by Learning Goal-Driven Instance Weights","date":"2020-06-22","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/effective-version-space-reduction-for","slug":"effective-version-space-reduction-for","title":"Effective Version Space Reduction for Convolutional Neural Networks","date":"2020-06-22","arxiv_id":"2006.12456","repositories_listed":0,"syntology":null},{"url":"/paper/rp2k-a-large-scale-retail-product-dataset","slug":"rp2k-a-large-scale-retail-product-dataset","title":"RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification","date":"2020-06-22","arxiv_id":"2006.12634","repositories_listed":0,"syntology":null},{"url":null,"slug":"gradient-em-bayesian-meta-learning","title":"Gradient-EM Bayesian Meta-learning","date":"2020-06-21","arxiv_id":"2006.11764","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-transfer-of-pose-estimation","title":"Adversarial Transfer of Pose Estimation Regression","date":"2020-06-20","arxiv_id":"2006.11658","repositories_listed":0,"syntology":null},{"url":null,"slug":"autood-automated-outlier-detection-via","title":"AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning","date":"2020-06-19","arxiv_id":"2006.11321","repositories_listed":0,"syntology":null},{"url":null,"slug":"covidlite-a-depth-wise-separable-deep-neural","title":"COVIDLite: A depth-wise separable deep neural network with white balance and CLAHE for detection of COVID-19","date":"2020-06-19","arxiv_id":"2006.13873","repositories_listed":0,"syntology":null},{"url":null,"slug":"keep-your-ai-es-on-the-road-tackling","title":"Keep Your AI-es on the Road: Tackling Distracted Driver Detection with Convolutional Neural Networks and Targeted Data Augmentation","date":"2020-06-19","arxiv_id":"2006.10955","repositories_listed":0,"syntology":null},{"url":null,"slug":"frost-filtered-scale-invariant-feature","title":"Frost filtered scale-invariant feature extraction and multilayer perceptron for hyperspectral image classification","date":"2020-06-18","arxiv_id":"2006.12556","repositories_listed":0,"syntology":null},{"url":null,"slug":"satimnet-structured-and-harmonised-training","title":"SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification","date":"2020-06-18","arxiv_id":"2006.10623","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-block-coordinate-descent-optimizer-for","title":"A block coordinate descent optimizer for classification problems exploiting convexity","date":"2020-06-17","arxiv_id":"2006.10123","repositories_listed":0,"syntology":null},{"url":null,"slug":"constraint-based-regularization-of-neural","title":"Constraint-Based Regularization of Neural Networks","date":"2020-06-17","arxiv_id":"2006.10114","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-subspace-neural-network-for-image","title":"Multi-Subspace Neural Network for Image Recognition","date":"2020-06-17","arxiv_id":"2006.09618","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-of-compositional-generalization-in","title":"A Study of Compositional Generalization in Neural Models","date":"2020-06-16","arxiv_id":"2006.09437","repositories_listed":0,"syntology":null},{"url":"/paper/fine-tuning-darts-for-image-classification","slug":"fine-tuning-darts-for-image-classification","title":"Fine-Tuning DARTS for Image Classification","date":"2020-06-16","arxiv_id":"2006.09042","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-federated-learning-the-case-of-affine","title":"Robust Federated Learning: The Case of Affine Distribution Shifts","date":"2020-06-16","arxiv_id":"2006.08907","repositories_listed":0,"syntology":null},{"url":"/paper/visual-and-textual-deep-feature-fusion-for","slug":"visual-and-textual-deep-feature-fusion-for","title":"Visual and Textual Deep Feature Fusion for Document Image Classification","date":"2020-06-16","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-optimal-convergence-rate-in","title":"Optimal Complexity in Decentralized Training","date":"2020-06-15","arxiv_id":"2006.08085","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentially-private-decentralized-learning","title":"Topology-aware Differential Privacy for Decentralized Image Classification","date":"2020-06-14","arxiv_id":"2006.07817","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-and-image-classification","title":"Domain Adaptation and Image Classification via Deep Conditional Adaptation Network","date":"2020-06-14","arxiv_id":"2006.07776","repositories_listed":0,"syntology":null},{"url":null,"slug":"explicitly-modeled-attention-maps-for-image","title":"Explicitly Modeled Attention Maps for Image Classification","date":"2020-06-14","arxiv_id":"2006.07872","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-approach-to-data-augmentation","title":"Meta Approach to Data Augmentation Optimization","date":"2020-06-14","arxiv_id":"2006.07965","repositories_listed":0,"syntology":null},{"url":null,"slug":"dtg-net-differentiated-teachers-guided-self","title":"DTG-Net: Differentiated Teachers Guided Self-Supervised Video Action Recognition","date":"2020-06-13","arxiv_id":"2006.07609","repositories_listed":0,"syntology":null},{"url":null,"slug":"split-merge-pooling","title":"Split-Merge Pooling","date":"2020-06-13","arxiv_id":"2006.07742","repositories_listed":0,"syntology":null},{"url":null,"slug":"attentive-feature-reuse-for-multi-task-meta","title":"Attentive Feature Reuse for Multi Task Meta learning","date":"2020-06-12","arxiv_id":"2006.07438","repositories_listed":0,"syntology":null},{"url":null,"slug":"bi-maml-balanced-incremental-approach-for","title":"BI-MAML: Balanced Incremental Approach for Meta Learning","date":"2020-06-12","arxiv_id":"2006.07412","repositories_listed":0,"syntology":null},{"url":null,"slug":"hmic-hierarchical-medical-image","title":"HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach","date":"2020-06-12","arxiv_id":"2006.07187","repositories_listed":0,"syntology":null},{"url":null,"slug":"move-to-data-a-new-continual-learning","title":"Move-to-Data: A new Continual Learning approach with Deep CNNs, Application for image-class recognition","date":"2020-06-12","arxiv_id":"2006.07152","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-compression","title":"Convolutional neural networks compression with low rank and sparse tensor decompositions","date":"2020-06-11","arxiv_id":"2006.06443","repositories_listed":0,"syntology":null},{"url":null,"slug":"diagnosis-and-analysis-of-celiac-disease-and","title":"Diagnosis and Analysis of Celiac Disease and Environmental Enteropathy on Biopsy Images using Deep Learning Approaches","date":"2020-06-11","arxiv_id":"2006.06627","repositories_listed":0,"syntology":null},{"url":null,"slug":"multigrid-in-channels-architectures-for-wide","title":"Multigrid-in-Channels Architectures for Wide Convolutional Neural Networks","date":"2020-06-11","arxiv_id":"2006.06799","repositories_listed":0,"syntology":null},{"url":null,"slug":"embedding-task-knowledge-into-3d-neural","title":"Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning","date":"2020-06-10","arxiv_id":"2006.05798","repositories_listed":0,"syntology":null},{"url":null,"slug":"bombus-species-image-classification","title":"Bombus Species Image Classification","date":"2020-06-09","arxiv_id":"2006.11374","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-shared-filter-bases-for-efficient","title":"Deeply Shared Filter Bases for Parameter-Efficient Convolutional Neural Networks","date":"2020-06-09","arxiv_id":"2006.05066","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-effectiveness-of-regularization","title":"On the Effectiveness of Regularization Against Membership Inference Attacks","date":"2020-06-09","arxiv_id":"2006.05336","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-curious-case-of-convex-networks","title":"The Curious Case of Convex Neural Networks","date":"2020-06-09","arxiv_id":"2006.05103","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-utilize-correlated-auxiliary","title":"Learning to Utilize Correlated Auxiliary Noise: A Possible Quantum Advantage","date":"2020-06-08","arxiv_id":"2006.04863","repositories_listed":0,"syntology":null},{"url":null,"slug":"provable-trade-offs-between-private-robust","title":"Trade-offs between membership privacy & adversarially robust learning","date":"2020-06-08","arxiv_id":"2006.04622","repositories_listed":0,"syntology":null},{"url":"/paper/deep-neural-networks-with-region-based","slug":"deep-neural-networks-with-region-based","title":"Deep Neural Networks With Region-Based Pooling Structures for Mammographic Image Classification","date":"2020-06-06","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-dynamic-networks-for","title":"Self-Supervised Dynamic Networks for Covariate Shift Robustness","date":"2020-06-06","arxiv_id":"2006.03952","repositories_listed":0,"syntology":null},{"url":"/paper/a-dataset-and-benchmarks-for-multimedia","slug":"a-dataset-and-benchmarks-for-multimedia","title":"A Dataset and Benchmarks for Multimedia Social Analysis","date":"2020-06-05","arxiv_id":"2006.08335","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simplified-2d-3d-cnn-architecture-for","title":"A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial–Spectral Fusion","date":"2020-06-05","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"acoustic-anomaly-detection-for-machine-sounds","title":"Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning","date":"2020-06-05","arxiv_id":"2006.03429","repositories_listed":0,"syntology":null},{"url":null,"slug":"content-and-context-features-for-scene-image","title":"Content and Context Features for Scene Image Representation","date":"2020-06-05","arxiv_id":"2006.03217","repositories_listed":0,"syntology":null},{"url":null,"slug":"discovering-parametric-activation-functions","title":"Discovering Parametric Activation Functions","date":"2020-06-05","arxiv_id":"2006.03179","repositories_listed":0,"syntology":null},{"url":null,"slug":"xai-for-graphs-explaining-graph-neural","title":"Higher-Order Explanations of Graph Neural Networks via Relevant Walks","date":"2020-06-05","arxiv_id":"2006.03589","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-sequential-feature-learning-in-clinical","title":"Deep Sequential Feature Learning in Clinical Image Classification of Infectious Keratitis","date":"2020-06-04","arxiv_id":"2006.02666","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-classification-in-the-dark-using-quanta","title":"Image Classification in the Dark using Quanta Image Sensors","date":"2020-06-03","arxiv_id":"2006.02026","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-multi-modal-nonlinear-embeddings","title":"Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm","date":"2020-06-03","arxiv_id":"2006.02330","repositories_listed":0,"syntology":null},{"url":null,"slug":"adinet-attribute-driven-incremental-network","title":"ADINet: Attribute Driven Incremental Network for Retinal Image Classification","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"badnl-backdoor-attacks-against-nlp-models","title":"BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements","date":"2020-06-01","arxiv_id":"2006.01043","repositories_listed":0,"syntology":null},{"url":null,"slug":"bfbox-searching-face-appropriate-backbone-and","title":"BFBox: Searching Face-Appropriate Backbone and Feature Pyramid Network for Face Detector","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-degradation-prior-for-low-quality-image","title":"Deep Degradation Prior for Low-Quality Image Classification","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deepemd-few-shot-image-classification-with-1","title":"DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover's Distance and Structured Classifiers","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/efficient-neural-vision-systems-based-on","slug":"efficient-neural-vision-systems-based-on","title":"Efficient Neural Vision Systems Based on Convolutional Image Acquisition","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"erasing-integrated-learning-a-simple-yet","title":"Erasing Integrated Learning: A Simple Yet Effective Approach for Weakly Supervised Object Localization","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"fixed-point-back-propagation-training","title":"Fixed-Point Back-Propagation Training","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"frequency-domain-compact-3d-convolutional","title":"Frequency Domain Compact 3D Convolutional Neural Networks","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"gp-nas-gaussian-process-based-neural","title":"GP-NAS: Gaussian Process Based Neural Architecture Search","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/hyperspectral-image-classification-of","slug":"hyperspectral-image-classification-of","title":"Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-domain-learning-with-dynamic-residual","title":"Latent Domain Learning with Dynamic Residual Adapters","date":"2020-06-01","arxiv_id":"2006.00996","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-augmentation-network-via-influence","title":"Learning Augmentation Network via Influence Functions","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-filter-pruning-criteria-for-deep","title":"Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"polishing-decision-based-adversarial-noise","title":"Polishing Decision-Based Adversarial Noise With a Customized Sampling","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-lymph-node-metastasis-using","title":"Predicting Lymph Node Metastasis Using Histopathological Images Based on Multiple Instance Learning With Deep Graph Convolution","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/probabilistic-structural-latent","slug":"probabilistic-structural-latent","title":"Probabilistic Structural Latent Representation for Unsupervised Embedding","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-computer-aided-tuberculosis","title":"Rethinking Computer-Aided Tuberculosis Diagnosis","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"rotation-consistent-margin-loss-for-efficient","title":"Rotation Consistent Margin Loss for Efficient Low-Bit Face Recognition","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"shoestring-graph-based-semi-supervised-1","title":"Shoestring: Graph-Based Semi-Supervised Classification With Severely Limited Labeled Data","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-verifying-robustness-of-neural-1","title":"Towards Verifying Robustness of Neural Networks Against A Family of Semantic Perturbations","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/weakly-supervised-fine-grained-image-1","slug":"weakly-supervised-fine-grained-image-1","title":"Weakly Supervised Fine-Grained Image Classification via Guassian Mixture Model Oriented Discriminative Learning","date":"2020-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"the-global-information-for-land-cover","title":"Integrating global spatial features in CNN based Hyperspectral/SAR imagery classification","date":"2020-05-30","arxiv_id":"2006.00234","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hierarchical-deep-convolutional-neural","title":"A Hierarchical Deep Convolutional Neural Network and Gated Recurrent Unit Framework for Structural Damage Detection","date":"2020-05-29","arxiv_id":"2006.01045","repositories_listed":0,"syntology":null},{"url":null,"slug":"overview-computer-vision-and-machine-learning","title":"Overview: Computer vision and machine learning for microstructural characterization and analysis","date":"2020-05-28","arxiv_id":"2005.14260","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerating-neural-network-inference-by","title":"Accelerating Neural Network Inference by Overflow Aware Quantization","date":"2020-05-27","arxiv_id":"2005.13297","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-adversarial-networks-for-bitcoin","title":"Generative Adversarial Networks for Bitcoin Data Augmentation","date":"2020-05-27","arxiv_id":"2005.13369","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-adversarial-logits-pairing","title":"Adaptive Adversarial Logits Pairing","date":"2020-05-25","arxiv_id":"2005.11904","repositories_listed":0,"syntology":null}],"record_sha256":"907dc32733d37ad3aa06629bbe62c5d975564a934263f1fde819b93dc361768f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}