{"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/72","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":72,"pages_in_order":105,"rows_per_page":100,"rows":[7101,7200],"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/71","next":"/task/image-classification/papers/73","papers":[{"url":null,"slug":"munet-evolving-pretrained-deep-neural","title":"muNet: Evolving Pretrained Deep Neural Networks into Scalable Auto-tuning Multitask Systems","date":"2022-05-22","arxiv_id":"2205.10937","repositories_listed":0,"syntology":null},{"url":null,"slug":"recent-advances-in-embedding-methods-for","title":"Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey","date":"2022-05-22","arxiv_id":"2205.10766","repositories_listed":0,"syntology":null},{"url":"/paper/deeper-vs-wider-a-revisit-of-transformer","slug":"deeper-vs-wider-a-revisit-of-transformer","title":"A Study on Transformer Configuration and Training Objective","date":"2022-05-21","arxiv_id":"2205.10505","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-transfer-learning-for-image","title":"Deep transfer learning for image classification: a survey","date":"2022-05-20","arxiv_id":"2205.09904","repositories_listed":0,"syntology":null},{"url":null,"slug":"set-based-meta-interpolation-for-few-task","title":"Set-based Meta-Interpolation for Few-Task Meta-Learning","date":"2022-05-20","arxiv_id":"2205.09990","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hardware-aware-framework-for-accelerating","title":"A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities","date":"2022-05-19","arxiv_id":"2205.10358","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-convolutional-neural-networks-for-2","title":"Bayesian Convolutional Neural Networks for Limited Data Hyperspectral Remote Sensing Image Classification","date":"2022-05-19","arxiv_id":"2205.09250","repositories_listed":0,"syntology":null},{"url":null,"slug":"incremental-learning-with-differentiable","title":"Incremental Learning with Differentiable Architecture and Forgetting Search","date":"2022-05-19","arxiv_id":"2205.09875","repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-learning-for-image","title":"Semi-Supervised Learning for Image Classification using Compact Networks in the BioMedical Context","date":"2022-05-19","arxiv_id":"2205.09678","repositories_listed":0,"syntology":null},{"url":null,"slug":"trt-vit-tensorrt-oriented-vision-transformer","title":"TRT-ViT: TensorRT-oriented Vision Transformer","date":"2022-05-19","arxiv_id":"2205.09579","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-neural-networks-learning-from-scratch","title":"Large Neural Networks Learning from Scratch with Very Few Data and without Explicit Regularization","date":"2022-05-18","arxiv_id":"2205.08836","repositories_listed":0,"syntology":null},{"url":null,"slug":"all-photonic-artificial-neural-network","title":"All-Photonic Artificial Neural Network Processor Via Non-linear Optics","date":"2022-05-17","arxiv_id":"2205.08608","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-graph-based-features-in","title":"Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer","date":"2022-05-17","arxiv_id":"2205.08467","repositories_listed":0,"syntology":null},{"url":null,"slug":"monotonicity-regularization-improved","title":"Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification","date":"2022-05-17","arxiv_id":"2205.08247","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncertainty-based-network-for-few-shot-image","title":"Uncertainty-based Network for Few-shot Image Classification","date":"2022-05-17","arxiv_id":"2205.08157","repositories_listed":0,"syntology":null},{"url":null,"slug":"binarizing-by-classification-is-soft-function","title":"Binarizing by Classification: Is soft function really necessary?","date":"2022-05-16","arxiv_id":"2205.07433","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-survey-on-model-quantization","title":"A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification","date":"2022-05-14","arxiv_id":"2205.07877","repositories_listed":0,"syntology":null},{"url":null,"slug":"corrosion-detection-for-industrial-objects","title":"Corrosion Detection for Industrial Objects: From Multi-Sensor System to 5D Feature Space","date":"2022-05-14","arxiv_id":"2205.07075","repositories_listed":0,"syntology":null},{"url":null,"slug":"practical-insights-of-repairing-model","title":"Practical Insights of Repairing Model Problems on Image Classification","date":"2022-05-14","arxiv_id":"2205.07116","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-convolutional-neural-network-1","title":"Analysis of convolutional neural network image classifiers in a rotationally symmetric model","date":"2022-05-11","arxiv_id":"2205.05500","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-safety-assurable-human-inspired-perception","title":"A Safety Assurable Human-Inspired Perception Architecture","date":"2022-05-10","arxiv_id":"2205.07862","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperparameter-optimization-of-hybrid-quantum","title":"Hybrid quantum ResNet for car classification and its hyperparameter optimization","date":"2022-05-10","arxiv_id":"2205.04878","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-medical-image-classification-from","title":"Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training","date":"2022-05-10","arxiv_id":"2205.04723","repositories_listed":0,"syntology":null},{"url":null,"slug":"vpn-verification-of-poisoning-in-neural","title":"VPN: Verification of Poisoning in Neural Networks","date":"2022-05-08","arxiv_id":"2205.03894","repositories_listed":0,"syntology":null},{"url":null,"slug":"large-scale-transfer-learning-for","title":"Large Scale Transfer Learning for Differentially Private Image Classification","date":"2022-05-06","arxiv_id":"2205.02973","repositories_listed":0,"syntology":null},{"url":null,"slug":"rcmnet-a-deep-learning-model-assists-car-t","title":"RCMNet: A deep learning model assists CAR-T therapy for leukemia","date":"2022-05-06","arxiv_id":"2205.04230","repositories_listed":0,"syntology":null},{"url":null,"slug":"biologically-inspired-deep-residual-networks","title":"Biologically inspired deep residual networks for computer vision applications","date":"2022-05-05","arxiv_id":"2205.02551","repositories_listed":0,"syntology":null},{"url":null,"slug":"mirst-dm-multi-instance-rst-with-drop-max","title":"MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer","date":"2022-05-02","arxiv_id":"2205.01674","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-generalization-capabilities-of-fsl","title":"On the generalization capabilities of FSL methods through domain adaptation: a case study in endoscopic kidney stone image classification","date":"2022-05-02","arxiv_id":"2205.00895","repositories_listed":0,"syntology":null},{"url":null,"slug":"augmentation-techniques-analysis-with-removal","title":"Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/improving-model-performance-and-removing-the","slug":"improving-model-performance-and-removing-the","title":"Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"resnet18-model-with-sequential-layer-for","title":"Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset","date":"2022-05-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"noise-reducing-attention-cross-fusion","title":"Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma","date":"2022-04-29","arxiv_id":"2204.13838","repositories_listed":0,"syntology":null},{"url":null,"slug":"pyramidclip-hierarchical-feature-alignment","title":"PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining","date":"2022-04-29","arxiv_id":"2204.14095","repositories_listed":0,"syntology":null},{"url":null,"slug":"continual-learning-with-bayesian-model-based","title":"Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor","date":"2022-04-28","arxiv_id":"2204.13349","repositories_listed":0,"syntology":null},{"url":null,"slug":"depth-estimation-with-simplified-transformer","title":"Depth Estimation with Simplified Transformer","date":"2022-04-28","arxiv_id":"2204.13791","repositories_listed":0,"syntology":null},{"url":null,"slug":"brain-tumor-detection-and-classification-1","title":"Brain Tumor Detection and Classification Using a New Evolutionary Convolutional Neural Network","date":"2022-04-26","arxiv_id":"2204.12297","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-classical-convolutional-neural","title":"Quantum-classical convolutional neural networks in radiological image classification","date":"2022-04-26","arxiv_id":"2204.12390","repositories_listed":0,"syntology":null},{"url":null,"slug":"ocformer-one-class-transformer-network-for","title":"OCFormer: One-Class Transformer Network for Image Classification","date":"2022-04-25","arxiv_id":"2204.11449","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-closer-look-at-personalization-in-federated","title":"A Closer Look at Personalization in Federated Image Classification","date":"2022-04-22","arxiv_id":"2204.11841","repositories_listed":0,"syntology":null},{"url":null,"slug":"balancing-expert-utilization-in-mixture-of","title":"Sparsely-gated Mixture-of-Expert Layers for CNN Interpretability","date":"2022-04-22","arxiv_id":"2204.10598","repositories_listed":0,"syntology":null},{"url":null,"slug":"icar-bridging-image-classification-and-image","title":"iCAR: Bridging Image Classification and Image-text Alignment for Visual Recognition","date":"2022-04-22","arxiv_id":"2204.10760","repositories_listed":0,"syntology":null},{"url":null,"slug":"reinforcing-generated-images-via-meta","title":"Reinforcing Generated Images via Meta-learning for One-Shot Fine-Grained Visual Recognition","date":"2022-04-22","arxiv_id":"2204.10689","repositories_listed":0,"syntology":null},{"url":null,"slug":"application-of-machine-learning-methods-to","title":"Enhancing Core Image Classification Using Generative Adversarial Networks (GANs)","date":"2022-04-21","arxiv_id":"2204.14224","repositories_listed":0,"syntology":null},{"url":null,"slug":"energy-efficient-classification-at-the","title":"Energy-Efficient Classification at the Wireless Edge with Reliability Guarantees","date":"2022-04-21","arxiv_id":"2204.10399","repositories_listed":0,"syntology":null},{"url":null,"slug":"gaf-nau-gramian-angular-field-encoded","title":"GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification","date":"2022-04-21","arxiv_id":"2204.10099","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-effnet-resnet-architectures-for","title":"Multiple EffNet/ResNet Architectures for Melanoma Classification","date":"2022-04-21","arxiv_id":"2204.10142","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-3-stage-spectral-spatial-method-for","title":"A 3-stage Spectral-spatial Method for Hyperspectral Image Classification","date":"2022-04-20","arxiv_id":"2204.09294","repositories_listed":0,"syntology":null},{"url":null,"slug":"robustness-testing-of-data-and-knowledge","title":"Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems","date":"2022-04-20","arxiv_id":"2204.09183","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-data-augmentation-for-deep-learning-a","title":"Image Data Augmentation for Deep Learning: A Survey","date":"2022-04-19","arxiv_id":"2204.08610","repositories_listed":0,"syntology":null},{"url":null,"slug":"optical-remote-sensing-image-understanding","title":"Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives","date":"2022-04-18","arxiv_id":"2204.09120","repositories_listed":0,"syntology":null},{"url":null,"slug":"privacy-preserving-image-classification-using","title":"Privacy-Preserving Image Classification Using Isotropic Network","date":"2022-04-16","arxiv_id":"2204.07707","repositories_listed":0,"syntology":null},{"url":null,"slug":"searching-intrinsic-dimensions-of-vision","title":"Searching Intrinsic Dimensions of Vision Transformers","date":"2022-04-16","arxiv_id":"2204.07722","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-of-degraded-acacia-tree-species","title":"Detection of Degraded Acacia tree species using deep neural networks on uav drone imagery","date":"2022-04-14","arxiv_id":"2204.07096","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-analysis-of-deep-learning-methods","title":"Explainable Analysis of Deep Learning Methods for SAR Image Classification","date":"2022-04-14","arxiv_id":"2204.06783","repositories_listed":0,"syntology":null},{"url":null,"slug":"hasa-hybrid-architecture-search-with","title":"HASA: Hybrid Architecture Search with Aggregation Strategy for Echinococcosis Classification and Ovary Segmentation in Ultrasound Images","date":"2022-04-14","arxiv_id":"2204.06697","repositories_listed":0,"syntology":null},{"url":null,"slug":"relaxing-equivariance-constraints-with-non","title":"Relaxing Equivariance Constraints with Non-stationary Continuous Filters","date":"2022-04-14","arxiv_id":"2204.07178","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-cross-attention-driven-spatial","title":"Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification","date":"2022-04-12","arxiv_id":"2204.05823","repositories_listed":0,"syntology":null},{"url":null,"slug":"examining-the-proximity-of-adversarial","title":"Examining the Proximity of Adversarial Examples to Class Manifolds in Deep Networks","date":"2022-04-12","arxiv_id":"2204.05764","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-equity-of-nuclear-norm-maximization-in","title":"On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation","date":"2022-04-12","arxiv_id":"2204.05596","repositories_listed":0,"syntology":null},{"url":null,"slug":"out-of-distribution-detection-in-unsupervised","title":"Out-Of-Distribution Detection In Unsupervised Continual Learning","date":"2022-04-12","arxiv_id":"2204.05462","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-simple-approach-to-adversarial-robustness-1","title":"A Simple Approach to Adversarial Robustness in Few-shot Image Classification","date":"2022-04-11","arxiv_id":"2204.05432","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-analysis-of-traditional-machine","title":"Comparison Analysis of Traditional Machine Learning and Deep Learning Techniques for Data and Image Classification","date":"2022-04-11","arxiv_id":"2204.05983","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-mutation-rate-adaptation-through","title":"Effective Mutation Rate Adaptation through Group Elite Selection","date":"2022-04-11","arxiv_id":"2204.04817","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-my-driver-observation-model-overconfident","title":"Is my Driver Observation Model Overconfident? Input-guided Calibration Networks for Reliable and Interpretable Confidence Estimates","date":"2022-04-10","arxiv_id":"2204.04674","repositories_listed":0,"syntology":null},{"url":"/paper/robust-cross-modal-representation-learning","slug":"robust-cross-modal-representation-learning","title":"Robust Cross-Modal Representation Learning with Progressive Self-Distillation","date":"2022-04-10","arxiv_id":"2204.04588","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowledge-free-black-box-watermark-and","title":"Knowledge-Free Black-Box Watermark and Ownership Proof for Image Classification Neural Networks","date":"2022-04-09","arxiv_id":"2204.04522","repositories_listed":0,"syntology":null},{"url":null,"slug":"does-robustness-on-imagenet-transfer-to","title":"Does Robustness on ImageNet Transfer to Downstream Tasks?","date":"2022-04-08","arxiv_id":"2204.03934","repositories_listed":0,"syntology":null},{"url":"/paper/multimodal-quasi-autoregression-forecasting","slug":"multimodal-quasi-autoregression-forecasting","title":"Multimodal Quasi-AutoRegression: Forecasting the visual popularity of new fashion products","date":"2022-04-08","arxiv_id":"2204.04014","repositories_listed":0,"syntology":null},{"url":null,"slug":"supernet-in-neural-architecture-search-a","title":"A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search","date":"2022-04-08","arxiv_id":"2204.03916","repositories_listed":0,"syntology":null},{"url":null,"slug":"deeptensor-low-rank-tensor-decomposition-with","title":"DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors","date":"2022-04-07","arxiv_id":"2204.03145","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-cross-domain-pretrained-model-for","title":"Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification","date":"2022-04-07","arxiv_id":"2204.03144","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-sample-z-mixup-richer-more-realistic","title":"Multi-Sample $ζ$-mixup: Richer, More Realistic Synthetic Samples from a $p$-Series Interpolant","date":"2022-04-07","arxiv_id":"2204.03323","repositories_listed":0,"syntology":null},{"url":null,"slug":"banana-sub-family-classification-and-quality","title":"Banana Sub-Family Classification and Quality Prediction using Computer Vision","date":"2022-04-06","arxiv_id":"2204.02581","repositories_listed":0,"syntology":null},{"url":null,"slug":"caipi-in-practice-towards-explainable","title":"CAIPI in Practice: Towards Explainable Interactive Medical Image Classification","date":"2022-04-06","arxiv_id":"2204.02661","repositories_listed":0,"syntology":null},{"url":null,"slug":"contextual-attention-mechanism-srgan-based","title":"Contextual Attention Mechanism, SRGAN Based Inpainting System for Eliminating Interruptions from Images","date":"2022-04-06","arxiv_id":"2204.02591","repositories_listed":0,"syntology":null},{"url":null,"slug":"latentgan-autoencoder-learning-disentangled","title":"LatentGAN Autoencoder: Learning Disentangled Latent Distribution","date":"2022-04-05","arxiv_id":"2204.02010","repositories_listed":0,"syntology":null},{"url":"/paper/attribute-prototype-network-for-any-shot","slug":"attribute-prototype-network-for-any-shot","title":"Attribute Prototype Network for Any-Shot Learning","date":"2022-04-04","arxiv_id":"2204.01208","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-zero-shot-learning-for-medical","title":"Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification","date":"2022-04-04","arxiv_id":"2204.01728","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-stable-are-transferability-metrics","title":"How stable are Transferability Metrics evaluations?","date":"2022-04-04","arxiv_id":"2204.01403","repositories_listed":0,"syntology":null},{"url":null,"slug":"kernel-extreme-learning-machine-optimized-by","title":"Kernel Extreme Learning Machine Optimized by the Sparrow Search Algorithm for Hyperspectral Image Classification","date":"2022-04-03","arxiv_id":"2204.00973","repositories_listed":0,"syntology":null},{"url":"/paper/revisiting-a-knn-based-image-classification","slug":"revisiting-a-knn-based-image-classification","title":"Revisiting a kNN-based Image Classification System with High-capacity Storage","date":"2022-04-03","arxiv_id":"2204.01186","repositories_listed":0,"syntology":null},{"url":null,"slug":"matching-feature-sets-for-few-shot-image","title":"Matching Feature Sets for Few-Shot Image Classification","date":"2022-04-02","arxiv_id":"2204.00949","repositories_listed":0,"syntology":null},{"url":null,"slug":"mix-up-self-supervised-learning-for-contrast","title":"Mix-up Self-Supervised Learning for Contrast-agnostic Applications","date":"2022-04-02","arxiv_id":"2204.00901","repositories_listed":0,"syntology":null},{"url":null,"slug":"conditional-autoregressors-are-interpretable","title":"Conditional Autoregressors are Interpretable Classifiers","date":"2022-03-31","arxiv_id":"2203.17002","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-maximal-coding-rate-reduction-by","title":"Efficient Maximal Coding Rate Reduction by Variational Forms","date":"2022-03-31","arxiv_id":"2204.00077","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-distillation-augmented-masked","title":"Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification","date":"2022-03-31","arxiv_id":"2203.16983","repositories_listed":0,"syntology":null},{"url":null,"slug":"sit-a-bionic-and-non-linear-neuron-for","title":"SIT: A Bionic and Non-Linear Neuron for Spiking Neural Network","date":"2022-03-30","arxiv_id":"2203.16117","repositories_listed":0,"syntology":null},{"url":null,"slug":"4weed-dataset-annotated-imagery-weeds-dataset","title":"4Weed Dataset: Annotated Imagery Weeds Dataset","date":"2022-03-29","arxiv_id":"2204.00080","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-neqr-processed-classical","title":"Classification of NEQR Processed Classical Images using Quantum Neural Networks (QNN)","date":"2022-03-29","arxiv_id":"2204.02797","repositories_listed":0,"syntology":null},{"url":null,"slug":"edge-detection-and-deep-learning-based-seti","title":"Edge Detection and Deep Learning Based SETI Signal Classification Method","date":"2022-03-29","arxiv_id":"2203.15229","repositories_listed":0,"syntology":null},{"url":null,"slug":"treatment-learning-transformer-for-noisy","title":"Treatment Learning Causal Transformer for Noisy Image Classification","date":"2022-03-29","arxiv_id":"2203.15529","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-fast-and-efficient-conditional-learning-for","title":"A Fast and Efficient Conditional Learning for Tunable Trade-Off between Accuracy and Robustness","date":"2022-03-28","arxiv_id":"2204.00426","repositories_listed":0,"syntology":null},{"url":null,"slug":"neurosymbolic-hybrid-approach-to-driver","title":"Neurosymbolic hybrid approach to driver collision warning","date":"2022-03-28","arxiv_id":"2203.15076","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-neural-tangent-kernel-analysis-of","title":"On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks","date":"2022-03-27","arxiv_id":"2203.14328","repositories_listed":0,"syntology":null},{"url":null,"slug":"give-me-your-attention-dot-product-attention","title":"Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness","date":"2022-03-25","arxiv_id":"2203.13639","repositories_listed":0,"syntology":null},{"url":null,"slug":"locally-asynchronous-stochastic-gradient","title":"Locally Asynchronous Stochastic Gradient Descent for Decentralised Deep Learning","date":"2022-03-24","arxiv_id":"2203.13085","repositories_listed":0,"syntology":null},{"url":null,"slug":"npc-neuron-path-coverage-via-characterizing","title":"NPC: Neuron Path Coverage via Characterizing Decision Logic of Deep Neural Networks","date":"2022-03-24","arxiv_id":"2203.12915","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-fixed-sub-center-a-better-way-to-capture","title":"The Fixed Sub-Center: A Better Way to Capture Data Complexity","date":"2022-03-24","arxiv_id":"2203.12928","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformers-meet-visual-learning","title":"Transformers Meet Visual Learning Understanding: A Comprehensive Review","date":"2022-03-24","arxiv_id":"2203.12944","repositories_listed":0,"syntology":null}],"record_sha256":"15935c876b2ba96c3385ac61dc0d6f1fbb5653afe361021a44b411cae4094306","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}