{"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/85","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":85,"pages_in_order":105,"rows_per_page":100,"rows":[8401,8500],"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/84","next":"/task/image-classification/papers/86","papers":[{"url":null,"slug":"from-artificial-intelligence-to-brain","title":"From Artificial Intelligence to Brain Intelligence: The basis learning and memory algorithm for brain-like intelligence","date":"2020-10-07","arxiv_id":"2010.14617","repositories_listed":0,"syntology":null},{"url":null,"slug":"slcrf-subspace-learning-with-conditional","title":"SLCRF: Subspace Learning with Conditional Random Field for Hyperspectral Image Classification","date":"2020-10-07","arxiv_id":"2010.03115","repositories_listed":0,"syntology":null},{"url":"/paper/variational-transfer-learning-for-fine","slug":"variational-transfer-learning-for-fine","title":"Variational Feature Disentangling for Fine-Grained Few-Shot Classification","date":"2020-10-07","arxiv_id":"2010.03255","repositories_listed":0,"syntology":null},{"url":null,"slug":"descriptive-analysis-of-computational-methods","title":"Descriptive analysis of computational methods for automating mammograms with practical applications","date":"2020-10-06","arxiv_id":"2010.03378","repositories_listed":0,"syntology":null},{"url":"/paper/domain-adaptive-transfer-learning-on-visual","slug":"domain-adaptive-transfer-learning-on-visual","title":"Domain Adaptive Transfer Learning on Visual Attention Aware Data Augmentation for Fine-grained Visual Categorization","date":"2020-10-06","arxiv_id":"2010.03071","repositories_listed":0,"syntology":null},{"url":null,"slug":"microscopic-fine-grained-instance","title":"Microscopic fine-grained instance classification through deep attention","date":"2020-10-06","arxiv_id":"2010.02818","repositories_listed":0,"syntology":null},{"url":null,"slug":"usable-information-and-evolution-of-optimal-1","title":"Usable Information and Evolution of Optimal Representations During Training","date":"2020-10-06","arxiv_id":"2010.02459","repositories_listed":0,"syntology":null},{"url":null,"slug":"visualizing-color-wise-saliency-of-black-box","title":"Visualizing Color-wise Saliency of Black-Box Image Classification Models","date":"2020-10-06","arxiv_id":"2010.02468","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-feature-learning-via-text","title":"Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging","date":"2020-10-06","arxiv_id":"2010.03060","repositories_listed":0,"syntology":null},{"url":null,"slug":"co2-consistent-contrast-for-unsupervised-1","title":"CO2: Consistent Contrast for Unsupervised Visual Representation Learning","date":"2020-10-05","arxiv_id":"2010.02217","repositories_listed":0,"syntology":null},{"url":null,"slug":"common-cnn-based-face-embedding-spaces-are","title":"Exploring the Interchangeability of CNN Embedding Spaces","date":"2020-10-05","arxiv_id":"2010.02323","repositories_listed":0,"syntology":null},{"url":null,"slug":"lipschitz-bounded-equilibrium-networks-1","title":"Lipschitz Bounded Equilibrium Networks","date":"2020-10-05","arxiv_id":"2010.01732","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixup-transfomer-dynamic-data-augmentation","title":"Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks","date":"2020-10-05","arxiv_id":"2010.02394","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-high-dimensional-memory-augmented","title":"Robust High-dimensional Memory-augmented Neural Networks","date":"2020-10-05","arxiv_id":"2010.01939","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-study-for-universal-adversarial-attacks-on","title":"A Study for Universal Adversarial Attacks on Texture Recognition","date":"2020-10-04","arxiv_id":"2010.01506","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-whitening-via-gradient-transformation","title":"Feature Whitening via Gradient Transformation for Improved Convergence","date":"2020-10-04","arxiv_id":"2010.01546","repositories_listed":0,"syntology":null},{"url":null,"slug":"consensus-clustering-with-unsupervised-1","title":"Consensus Clustering With Unsupervised Representation Learning","date":"2020-10-03","arxiv_id":"2010.01245","repositories_listed":0,"syntology":null},{"url":null,"slug":"ucp-uniform-channel-pruning-for-deep","title":"UCP: Uniform Channel Pruning for Deep Convolutional Neural Networks Compression and Acceleration","date":"2020-10-03","arxiv_id":"2010.01251","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-knowledge-distillation-via-multi","title":"Online Knowledge Distillation via Multi-branch Diversity Enhancement","date":"2020-10-02","arxiv_id":"2010.00795","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-quality-remote-sensing-image-super","title":"High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network","date":"2020-10-01","arxiv_id":"2010.00472","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-roc-and-unlabeled-data-for-increasing","title":"Using Unlabeled Data for Increasing Low-Shot Classification Accuracy of Relevant and Open-Set Irrelevant Images","date":"2020-10-01","arxiv_id":"2010.00721","repositories_listed":0,"syntology":null},{"url":null,"slug":"ask-n-learn-active-learning-via-reliable","title":"Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification","date":"2020-09-30","arxiv_id":"2009.14448","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-aware-noisy-label-learning-for","title":"Attention-Aware Noisy Label Learning for Image Classification","date":"2020-09-30","arxiv_id":"2009.14757","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-implementation-of-rmnv2-classifier","title":"Real-time Implementation of RMNv2 Classifier in NXP Bluebox 2.0 and NXP i.MX RT1060","date":"2020-09-30","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-precision-ensemble-self-knowledge","title":"Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks","date":"2020-09-30","arxiv_id":"2009.14502","repositories_listed":0,"syntology":null},{"url":null,"slug":"strategy-and-benchmark-for-converting-deep-q","title":"Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks","date":"2020-09-30","arxiv_id":"2009.14456","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comparative-study-of-deep-learning-loss","title":"A Comparative Study of Deep Learning Loss Functions for Multi-Label Remote Sensing Image Classification","date":"2020-09-29","arxiv_id":"2009.13935","repositories_listed":0,"syntology":null},{"url":null,"slug":"learn-like-a-pathologist-curriculum-learning","title":"Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification","date":"2020-09-29","arxiv_id":"2009.13698","repositories_listed":0,"syntology":null},{"url":null,"slug":"trustworthy-convolutional-neural-networks-a","title":"Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach","date":"2020-09-29","arxiv_id":"2009.14260","repositories_listed":0,"syntology":null},{"url":null,"slug":"where-is-the-model-looking-at-concentrate-and","title":"Where is the Model Looking At?--Concentrate and Explain the Network Attention","date":"2020-09-29","arxiv_id":"2009.13862","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-risk-minimization-a-meta-learning-1","title":"Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift","date":"2020-09-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predicting-the-outputs-of-finite-networks-1","title":"Predicting the Outputs of Finite Networks Trained with Noisy Gradients","date":"2020-09-28","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/scalable-transfer-learning-with-expert-models","slug":"scalable-transfer-learning-with-expert-models","title":"Scalable Transfer Learning with Expert Models","date":"2020-09-28","arxiv_id":"2009.13239","repositories_listed":0,"syntology":null},{"url":null,"slug":"where-does-the-robustness-come-from-a-study","title":"Where Does the Robustness Come from? A Study of the Transformation-based Ensemble Defence","date":"2020-09-28","arxiv_id":"2009.13033","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-realistic-covid19-x-rays-with-a","title":"Generating Realistic COVID19 X-rays with a Mean Teacher + Transfer Learning GAN","date":"2020-09-26","arxiv_id":"2009.12478","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-unified-plug-and-play-framework-for","title":"A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention","date":"2020-09-25","arxiv_id":"2009.12027","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-cnns-in-presence-of-jpeg-compression","title":"Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision","date":"2020-09-25","arxiv_id":"2009.12088","repositories_listed":0,"syntology":null},{"url":null,"slug":"pk-gcn-prior-knowledge-assisted-image","title":"PK-GCN: Prior Knowledge Assisted Image Classification using Graph Convolution Networks","date":"2020-09-24","arxiv_id":"2009.11892","repositories_listed":0,"syntology":null},{"url":null,"slug":"compressive-spectral-image-classification","title":"Compressive spectral image classification using 3D coded convolutional neural network","date":"2020-09-23","arxiv_id":"2009.11948","repositories_listed":0,"syntology":null},{"url":null,"slug":"pruning-convolutional-filters-using-batch","title":"Pruning Convolutional Filters using Batch Bridgeout","date":"2020-09-23","arxiv_id":"2009.10893","repositories_listed":0,"syntology":null},{"url":null,"slug":"dynamic-fusion-based-federated-learning-for","title":"Dynamic Fusion based Federated Learning for COVID-19 Detection","date":"2020-09-22","arxiv_id":"2009.10401","repositories_listed":0,"syntology":null},{"url":null,"slug":"integration-of-roadside-camera-images-and","title":"Integration of Roadside Camera Images and Weather Data for Monitoring Winter Road Surface Conditions","date":"2020-09-22","arxiv_id":"2009.12165","repositories_listed":0,"syntology":null},{"url":null,"slug":"role-of-orthogonality-constraints-in","title":"Role of Orthogonality Constraints in Improving Properties of Deep Networks for Image Classification","date":"2020-09-22","arxiv_id":"2009.10762","repositories_listed":0,"syntology":null},{"url":null,"slug":"using-neural-architecture-search-for","title":"Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models","date":"2020-09-22","arxiv_id":"2009.10644","repositories_listed":0,"syntology":null},{"url":"/paper/the-dongniao-international-birds-10000","slug":"the-dongniao-international-birds-10000","title":"The DongNiao International Birds 10000 Dataset","date":"2020-09-21","arxiv_id":"2010.06454","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-semantic-embedding-model-informed-by","title":"Visual-Semantic Embedding Model Informed by Structured Knowledge","date":"2020-09-21","arxiv_id":"2009.10026","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-soft-labels-via-meta-learning","title":"Learning Soft Labels via Meta Learning","date":"2020-09-20","arxiv_id":"2009.09496","repositories_listed":0,"syntology":null},{"url":null,"slug":"features-based-mammogram-image-classification","title":"Features based Mammogram Image Classification using Weighted Feature Support Vector Machine","date":"2020-09-19","arxiv_id":"2009.09300","repositories_listed":0,"syntology":null},{"url":null,"slug":"it-s-raining-cats-or-dogs-adversarial-rain","title":"Adversarial Rain Attack and Defensive Deraining for DNN Perception","date":"2020-09-19","arxiv_id":"2009.09205","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-level-graph-convolutional-network-with","title":"Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification","date":"2020-09-19","arxiv_id":"2009.09196","repositories_listed":0,"syntology":null},{"url":null,"slug":"addersr-towards-energy-efficient-image-super","title":"AdderSR: Towards Energy Efficient Image Super-Resolution","date":"2020-09-18","arxiv_id":"2009.08891","repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-efficient-dnn-ensembles-with","title":"Generating Efficient DNN-Ensembles with Evolutionary Computation","date":"2020-09-18","arxiv_id":"2009.08698","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspectral-image-classification-method","title":"Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion","date":"2020-09-18","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-collective-learning-learning-optimal","title":"Deep Collective Learning: Learning Optimal Inputs and Weights Jointly in Deep Neural Networks","date":"2020-09-17","arxiv_id":"2009.07988","repositories_listed":0,"syntology":null},{"url":null,"slug":"eating-habits-discovery-in-egocentric-photo","title":"Eating Habits Discovery in Egocentric Photo-streams","date":"2020-09-16","arxiv_id":"2009.07646","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-image-classification-through","title":"Unsupervised Image Classification Through Time-Multiplexed Photonic Multi-Layer Spiking Convolutional Neural Network","date":"2020-09-16","arxiv_id":"2009.08309","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-the-equation-of-state-from","title":"Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning","date":"2020-09-15","arxiv_id":"2009.07367","repositories_listed":0,"syntology":null},{"url":null,"slug":"ensemble-learning-of-diffractive-optical","title":"Ensemble learning of diffractive optical networks","date":"2020-09-15","arxiv_id":"2009.06869","repositories_listed":0,"syntology":null},{"url":null,"slug":"margin-based-regularization-and-selective","title":"Margin-Based Regularization and Selective Sampling in Deep Neural Networks","date":"2020-09-13","arxiv_id":"2009.06011","repositories_listed":0,"syntology":null},{"url":null,"slug":"polsar-image-classification-based-on-robust","title":"PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field","date":"2020-09-13","arxiv_id":"2009.05942","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-convolutional-neural-network","title":"An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks","date":"2020-09-11","arxiv_id":"2009.05236","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-shape-features-and-abstractions-in","title":"Learning Shape Features and Abstractions in 3D Convolutional Neural Networks for Detecting Alzheimer's Disease","date":"2020-09-10","arxiv_id":"2009.05023","repositories_listed":0,"syntology":null},{"url":null,"slug":"prune-responsibly","title":"Prune Responsibly","date":"2020-09-10","arxiv_id":"2009.09936","repositories_listed":0,"syntology":null},{"url":"/paper/quantnet-learning-to-quantize-by-learning","slug":"quantnet-learning-to-quantize-by-learning","title":"QuantNet: Learning to Quantize by Learning within Fully Differentiable Framework","date":"2020-09-10","arxiv_id":"2009.04626","repositories_listed":0,"syntology":null},{"url":null,"slug":"improved-trainable-calibration-method-for","title":"Improved Trainable Calibration Method for Neural Networks on Medical Imaging Classification","date":"2020-09-09","arxiv_id":"2009.04057","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-machine-learning-in-image","title":"Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective","date":"2020-09-08","arxiv_id":"2009.03728","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-hyperspectral-image-classification","title":"Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learning","date":"2020-09-08","arxiv_id":"2009.03508","repositories_listed":0,"syntology":null},{"url":null,"slug":"tanhsoft-a-family-of-activation-functions","title":"TanhSoft -- a family of activation functions combining Tanh and Softplus","date":"2020-09-08","arxiv_id":"2009.03863","repositories_listed":0,"syntology":null},{"url":null,"slug":"black-box-to-white-box-discover-model","title":"Black Box to White Box: Discover Model Characteristics Based on Strategic Probing","date":"2020-09-07","arxiv_id":"2009.03136","repositories_listed":0,"syntology":null},{"url":null,"slug":"representativity-and-consistency-measures-for","title":"How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks","date":"2020-09-07","arxiv_id":"2009.04521","repositories_listed":0,"syntology":null},{"url":null,"slug":"pso-ps-parameter-synchronization-with","title":"PSO-PS: Parameter Synchronization with Particle Swarm Optimization for Distributed Training of Deep Neural Networks","date":"2020-09-06","arxiv_id":"2009.03816","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-efficient-technique-for-image-captioning","title":"An Efficient Technique for Image Captioning using Deep Neural Network","date":"2020-09-05","arxiv_id":"2009.02565","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-augmentation-for-electrocardiogram","title":"Data Augmentation for Electrocardiogram Classification with Deep Neural Network","date":"2020-09-05","arxiv_id":"2009.04398","repositories_listed":0,"syntology":null},{"url":null,"slug":"s-sgd-symmetrical-stochastic-gradient-descent","title":"S-SGD: Symmetrical Stochastic Gradient Descent with Weight Noise Injection for Reaching Flat Minima","date":"2020-09-05","arxiv_id":"2009.02479","repositories_listed":0,"syntology":null},{"url":null,"slug":"acdc-weight-sharing-in-atom-coefficient","title":"ACDC: Weight Sharing in Atom-Coefficient Decomposed Convolution","date":"2020-09-04","arxiv_id":"2009.02386","repositories_listed":0,"syntology":null},{"url":null,"slug":"explanation-of-unintended-radiated-emission","title":"Explanation of Unintended Radiated Emission Classification via LIME","date":"2020-09-04","arxiv_id":"2009.02418","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-online-optimization-via-gradient","title":"GTAdam: Gradient Tracking with Adaptive Momentum for Distributed Online Optimization","date":"2020-09-03","arxiv_id":"2009.01745","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-visual-study-of-multiple","title":"Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images","date":"2020-09-03","arxiv_id":"2009.02256","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-brain-tumor-classification","title":"Multimodal brain tumor classification","date":"2020-09-03","arxiv_id":"2009.01592","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-the-brittleness-of-ai-safety","title":"Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance","date":"2020-09-02","arxiv_id":"2009.00802","repositories_listed":0,"syntology":null},{"url":null,"slug":"select-protonet-learning-to-select-for-few","title":"Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction","date":"2020-09-02","arxiv_id":"2009.00792","repositories_listed":0,"syntology":null},{"url":null,"slug":"yet-meta-learning-can-adapt-fast-it-can-also","title":"Yet Meta Learning Can Adapt Fast, It Can Also Break Easily","date":"2020-09-02","arxiv_id":"2009.01672","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-deep-densely-connected-convolutional","title":"Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification","date":"2020-09-01","arxiv_id":"2009.00320","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-learning-techniques-for-geospatial-data","title":"Deep Learning Techniques for Geospatial Data Analysis","date":"2020-08-30","arxiv_id":"2008.13146","repositories_listed":0,"syntology":null},{"url":null,"slug":"synthetic-sample-selection-via-reinforcement","title":"Synthetic Sample Selection via Reinforcement Learning","date":"2020-08-26","arxiv_id":"2008.11331","repositories_listed":0,"syntology":null},{"url":null,"slug":"visual-concept-reasoning-networks","title":"Visual Concept Reasoning Networks","date":"2020-08-26","arxiv_id":"2008.11783","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-evolutionary-neural-architecture","title":"A Survey on Evolutionary Neural Architecture Search","date":"2020-08-25","arxiv_id":"2008.10937","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-noncoding-rna-elements","title":"Classification of Noncoding RNA Elements Using Deep Convolutional Neural Networks","date":"2020-08-24","arxiv_id":"2008.10580","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-disease-classification-via-weakly","title":"Explainable Disease Classification via weakly-supervised segmentation","date":"2020-08-24","arxiv_id":"2008.10268","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-kernel-for-conditional-moment","title":"Learning Kernel for Conditional Moment-Matching Discrepancy-based Image Classification","date":"2020-08-24","arxiv_id":"2008.10165","repositories_listed":0,"syntology":null},{"url":"/paper/few-shot-image-classification-via-contrastive","slug":"few-shot-image-classification-via-contrastive","title":"Few-Shot Image Classification via Contrastive Self-Supervised Learning","date":"2020-08-23","arxiv_id":"2008.09942","repositories_listed":0,"syntology":null},{"url":"/paper/memory-based-jitter-improving-visual","slug":"memory-based-jitter-improving-visual","title":"Memory-based Jitter: Improving Visual Recognition on Long-tailed Data with Diversity In Memory","date":"2020-08-22","arxiv_id":"2008.09809","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-weight-bitwidth-to-rule-them-all","title":"One Weight Bitwidth to Rule Them All","date":"2020-08-22","arxiv_id":"2008.09916","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-assessing-the-generalization","title":"A Survey on Assessing the Generalization Envelope of Deep Neural Networks: Predictive Uncertainty, Out-of-distribution and Adversarial Samples","date":"2020-08-21","arxiv_id":"2008.09381","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-neural-networks-for-unsuperviseddomain","title":"Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics","date":"2020-08-21","arxiv_id":"2008.09304","repositories_listed":0,"syntology":null},{"url":null,"slug":"icaps-an-interpretable-classifier-via","title":"iCaps: An Interpretable Classifier via Disentangled Capsule Networks","date":"2020-08-20","arxiv_id":"2008.08756","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-spectral-ffpnet-attention-based","title":"Spatial--spectral FFPNet: Attention-Based Pyramid Network for Segmentation and Classification of Remote Sensing Images","date":"2020-08-20","arxiv_id":"2008.08775","repositories_listed":0,"syntology":null},{"url":null,"slug":"utilizing-explainable-ai-for-quantization-and","title":"Utilizing Explainable AI for Quantization and Pruning of Deep Neural Networks","date":"2020-08-20","arxiv_id":"2008.09072","repositories_listed":0,"syntology":null},{"url":null,"slug":"correcting-data-imbalance-for-semi-supervised","title":"Correcting Data Imbalance for Semi-Supervised Covid-19 Detection Using X-ray Chest Images","date":"2020-08-19","arxiv_id":"2008.08496","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-connectivity-of-neural-networks-from","title":"Learning Connectivity of Neural Networks from a Topological Perspective","date":"2020-08-19","arxiv_id":"2008.08261","repositories_listed":0,"syntology":null}],"record_sha256":"1f011f2406a4b68388887b9fee9875b8d455d947e3fd34bef9e45f8c841a6e30","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}