{"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/52","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":52,"pages_in_order":105,"rows_per_page":100,"rows":[5101,5200],"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/51","next":"/task/image-classification/papers/53","papers":[{"url":null,"slug":"embeddings-are-all-you-need-achieving-high","title":"Embeddings are all you need! Achieving High Performance Medical Image Classification through Training-Free Embedding Analysis","date":"2024-12-12","arxiv_id":"2412.09445","repositories_listed":0,"syntology":null},{"url":null,"slug":"steam-squeeze-and-transform-enhanced","title":"STEAM: Squeeze and Transform Enhanced Attention Module","date":"2024-12-12","arxiv_id":"2412.09023","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-learning-of-non-conjugate","title":"Stochastic Learning of Non-Conjugate Variational Posterior for Image Classification","date":"2024-12-12","arxiv_id":"2412.08951","repositories_listed":0,"syntology":null},{"url":null,"slug":"alore-efficient-visual-adaptation-via","title":"ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Experts","date":"2024-12-11","arxiv_id":"2412.08341","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-approaches-to-fair-image","title":"Multimodal Approaches to Fair Image Classification: An Ethical Perspective","date":"2024-12-11","arxiv_id":"2412.12165","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-enhancement-of-cnn-algorithm-for-rice-leaf","title":"An Enhancement of CNN Algorithm for Rice Leaf Disease Image Classification in Mobile Applications","date":"2024-12-10","arxiv_id":"2412.07182","repositories_listed":0,"syntology":null},{"url":null,"slug":"fastdds-based-middleware-system-for-remote-x","title":"Real-time Chest X-Ray Distributed Decision Support for Resource-constrained Clinics","date":"2024-12-10","arxiv_id":"2412.07818","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-training-non-uniform-quantization-for","title":"Post-Training Non-Uniform Quantization for Convolutional Neural Networks","date":"2024-12-10","arxiv_id":"2412.07391","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolution-goes-higher-order-a-biologically","title":"Convolution goes higher-order: a biologically inspired mechanism empowers image classification","date":"2024-12-09","arxiv_id":"2412.06740","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-privacy-parameters-on-deep-learning","title":"Impact of Privacy Parameters on Deep Learning Models for Image Classification","date":"2024-12-09","arxiv_id":"2412.06689","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyperspectral-image-spectral-spatial-feature","title":"Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis","date":"2024-12-08","arxiv_id":"2412.06075","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-transformer-based-semantic","title":"Vision Transformer-based Semantic Communications With Importance-Aware Quantization","date":"2024-12-08","arxiv_id":"2412.06038","repositories_listed":0,"syntology":null},{"url":null,"slug":"mtspark-enabling-multi-task-learning-with","title":"MTSpark: Enabling Multi-Task Learning with Spiking Neural Networks for Generalist Agents","date":"2024-12-06","arxiv_id":"2412.04847","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-whole-slide-image-classification","title":"Enhancing Whole Slide Image Classification through Supervised Contrastive Domain Adaptation","date":"2024-12-05","arxiv_id":"2412.04260","repositories_listed":0,"syntology":null},{"url":null,"slug":"multisource-collaborative-domain","title":"Multisource Collaborative Domain Generalization for Cross-Scene Remote Sensing Image Classification","date":"2024-12-05","arxiv_id":"2412.03897","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantized-and-interpretable-learning-scheme","title":"Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task","date":"2024-12-05","arxiv_id":"2412.03915","repositories_listed":0,"syntology":null},{"url":null,"slug":"assessing-the-performance-of-ct-image","title":"Assessing the performance of CT image denoisers using Laguerre-Gauss Channelized Hotelling Observer for lesion detection","date":"2024-12-04","arxiv_id":"2412.02920","repositories_listed":0,"syntology":null},{"url":null,"slug":"higher-order-transformers-efficient-attention","title":"Higher Order Transformers: Efficient Attention Mechanism for Tensor Structured Data","date":"2024-12-04","arxiv_id":"2412.02919","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-classic-quantum-hybrid-network-framework","title":"Lean classical-quantum hybrid neural network model for image classification","date":"2024-12-03","arxiv_id":"2412.02059","repositories_listed":0,"syntology":null},{"url":null,"slug":"genmix-effective-data-augmentation-with","title":"GenMix: Effective Data Augmentation with Generative Diffusion Model Image Editing","date":"2024-12-03","arxiv_id":"2412.02366","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixture-of-physical-priors-adapter-for","title":"Mixture of Physical Priors Adapter for Parameter-Efficient Fine-Tuning","date":"2024-12-03","arxiv_id":"2412.02759","repositories_listed":0,"syntology":null},{"url":null,"slug":"synergistic-development-of-perovskite","title":"Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing","date":"2024-12-03","arxiv_id":"2412.02779","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-distance-weighted-cross-entropy-loss-1","title":"Class Distance Weighted Cross Entropy Loss for Classification of Disease Severity","date":"2024-12-02","arxiv_id":"2412.01246","repositories_listed":0,"syntology":null},{"url":null,"slug":"explaining-the-unexplained-revealing-hidden","title":"Explaining the Unexplained: Revealing Hidden Correlations for Better Interpretability","date":"2024-12-02","arxiv_id":"2412.01365","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuron-abandoning-attention-flow-visual","title":"Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Models","date":"2024-12-02","arxiv_id":"2412.01202","repositories_listed":0,"syntology":null},{"url":null,"slug":"profit-a-proximal-fine-tuning-optimizer-for","title":"PROFIT: A Specialized Optimizer for Deep Fine Tuning","date":"2024-12-02","arxiv_id":"2412.01930","repositories_listed":0,"syntology":null},{"url":null,"slug":"spf-net-solar-panel-fault-detection-using-u","title":"SPF-Net: Solar panel fault detection using U-Net based deep learning image classification","date":"2024-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-privacy-preserving-medical-imaging","title":"Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture","date":"2024-12-01","arxiv_id":"2412.00687","repositories_listed":0,"syntology":null},{"url":null,"slug":"curriculum-fine-tuning-of-vision-foundation","title":"Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise","date":"2024-11-29","arxiv_id":"2412.00150","repositories_listed":0,"syntology":null},{"url":null,"slug":"fairdd-fair-dataset-distillation-via","title":"FairDD: Fair Dataset Distillation via Synchronized Matching","date":"2024-11-29","arxiv_id":"2411.19623","repositories_listed":0,"syntology":null},{"url":null,"slug":"mvformer-diversifying-feature-normalization","title":"MVFormer: Diversifying Feature Normalization and Token Mixing for Efficient Vision Transformers","date":"2024-11-28","arxiv_id":"2411.18995","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-attention-vectors-generative","title":"Sparse Attention Vectors: Generative Multimodal Model Features Are Discriminative Vision-Language Classifiers","date":"2024-11-28","arxiv_id":"2412.00142","repositories_listed":0,"syntology":null},{"url":null,"slug":"fall-leaf-adversarial-attack-on-traffic-sign","title":"Fall Leaf Adversarial Attack on Traffic Sign Classification","date":"2024-11-27","arxiv_id":"2411.18776","repositories_listed":0,"syntology":null},{"url":null,"slug":"kans-for-computer-vision-an-experimental","title":"KANs for Computer Vision: An Experimental Study","date":"2024-11-27","arxiv_id":"2411.18224","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-semi-supervised-learning-to","title":"Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data","date":"2024-11-27","arxiv_id":"2411.18622","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixture-of-experts-in-image-classification","title":"Mixture of Experts in Image Classification: What's the Sweet Spot?","date":"2024-11-27","arxiv_id":"2411.18322","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimized-tradeoffs-for-private-prediction","title":"Optimized Tradeoffs for Private Prediction with Majority Ensembling","date":"2024-11-27","arxiv_id":"2411.17965","repositories_listed":0,"syntology":null},{"url":null,"slug":"pruning-deep-convolutional-neural-network","title":"Pruning Deep Convolutional Neural Network Using Conditional Mutual Information","date":"2024-11-27","arxiv_id":"2411.18578","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-in-depth-investigation-of-sparse-rate","title":"An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models","date":"2024-11-26","arxiv_id":"2411.17182","repositories_listed":0,"syntology":null},{"url":null,"slug":"badscan-an-architectural-backdoor-attack-on","title":"BadScan: An Architectural Backdoor Attack on Visual State Space Models","date":"2024-11-26","arxiv_id":"2411.17283","repositories_listed":0,"syntology":null},{"url":null,"slug":"spikeatconv-an-integrated-spiking","title":"SpikeAtConv: An Integrated Spiking-Convolutional Attention Architecture for Energy-Efficient Neuromorphic Vision Processing","date":"2024-11-26","arxiv_id":"2411.17439","repositories_listed":0,"syntology":null},{"url":null,"slug":"debiasing-classifiers-by-amplifying-bias-with","title":"Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models","date":"2024-11-25","arxiv_id":"2411.16079","repositories_listed":0,"syntology":null},{"url":null,"slug":"creating-scalable-agi-the-open-general","title":"Creating Scalable AGI: the Open General Intelligence Framework","date":"2024-11-24","arxiv_id":"2411.15832","repositories_listed":0,"syntology":null},{"url":null,"slug":"twin-trigger-generative-networks-for-backdoor","title":"Twin Trigger Generative Networks for Backdoor Attacks against Object Detection","date":"2024-11-23","arxiv_id":"2411.15439","repositories_listed":0,"syntology":null},{"url":null,"slug":"megl-multimodal-explanation-guided-learning","title":"MEGL: Multimodal Explanation-Guided Learning","date":"2024-11-20","arxiv_id":"2411.13053","repositories_listed":0,"syntology":null},{"url":null,"slug":"uni-mlip-unified-self-supervision-for-medical","title":"Uni-Mlip: Unified Self-supervision for Medical Vision Language Pre-training","date":"2024-11-20","arxiv_id":"2411.15207","repositories_listed":0,"syntology":null},{"url":null,"slug":"problem-dependent-convergence-bounds-for","title":"Problem-dependent convergence bounds for randomized linear gradient compression","date":"2024-11-19","arxiv_id":"2411.12898","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-in-deep-networks-a","title":"Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification","date":"2024-11-19","arxiv_id":"2411.12151","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-emerging-trends-and-research","title":"Exploring Emerging Trends and Research Opportunities in Visual Place Recognition","date":"2024-11-18","arxiv_id":"2411.11481","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-distillation-teaching-fairness-from","title":"Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging","date":"2024-11-18","arxiv_id":"2411.11939","repositories_listed":0,"syntology":null},{"url":null,"slug":"just-leaf-it-accelerating-diffusion","title":"Just Leaf It: Accelerating Diffusion Classifiers with Hierarchical Class Pruning","date":"2024-11-18","arxiv_id":"2411.12073","repositories_listed":0,"syntology":null},{"url":null,"slug":"diagnostic-text-guided-representation","title":"Diagnostic Text-guided Representation Learning in Hierarchical Classification for Pathological Whole Slide Image","date":"2024-11-16","arxiv_id":"2411.10709","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-perspective-contrastive-logit","title":"Multi-perspective Contrastive Logit Distillation","date":"2024-11-16","arxiv_id":"2411.10693","repositories_listed":0,"syntology":null},{"url":null,"slug":"adapting-the-biological-ssvep-response-to","title":"Adapting the Biological SSVEP Response to Artificial Neural Networks","date":"2024-11-15","arxiv_id":"2411.10084","repositories_listed":0,"syntology":null},{"url":null,"slug":"evidential-federated-learning-for-skin-lesion","title":"Evidential Federated Learning for Skin Lesion Image Classification","date":"2024-11-15","arxiv_id":"2411.10071","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-cost-of-model-serving-frameworks-an","title":"On the Cost of Model-Serving Frameworks: An Experimental Evaluation","date":"2024-11-15","arxiv_id":"2411.10337","repositories_listed":0,"syntology":null},{"url":null,"slug":"outliers-resistant-image-classification-by","title":"Outliers resistant image classification by anomaly detection","date":"2024-11-15","arxiv_id":"2411.10150","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-attacks-using-differentiable","title":"RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering","date":"2024-11-14","arxiv_id":"2411.09749","repositories_listed":0,"syntology":null},{"url":null,"slug":"residualdroppath-enhancing-feature-reuse-over","title":"ResidualDroppath: Enhancing Feature Reuse over Residual Connections","date":"2024-11-14","arxiv_id":"2411.09475","repositories_listed":0,"syntology":null},{"url":null,"slug":"computed-tomography-using-meta-optics","title":"Computed tomography using meta-optics","date":"2024-11-13","arxiv_id":"2411.08995","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-whole-slide-image-classification","title":"Efficient Whole Slide Image Classification through Fisher Vector Representation","date":"2024-11-13","arxiv_id":"2411.08530","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-on-multi-resolution","title":"Semantic segmentation on multi-resolution optical and microwave data using deep learning","date":"2024-11-12","arxiv_id":"2411.07581","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-kan-work-exploring-the-potential-of","title":"Can KAN Work? Exploring the Potential of Kolmogorov-Arnold Networks in Computer Vision","date":"2024-11-11","arxiv_id":"2411.06727","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-active-learning-in-the-open-world","title":"Deep Active Learning in the Open World","date":"2024-11-10","arxiv_id":"2411.06353","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-compass-a-comprehensive-and-effective","title":"AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for AI Systems","date":"2024-11-09","arxiv_id":"2411.06146","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-structural-nonlinearity-in-binary","title":"Exploring Structural Nonlinearity in Binary Polariton-Based Neuromorphic Architectures","date":"2024-11-09","arxiv_id":"2411.06124","repositories_listed":0,"syntology":null},{"url":null,"slug":"mutual-energy-inner-product-optimization","title":"Mutual-energy inner product optimization method for constructing feature coordinates and image classification in Machine Learning","date":"2024-11-09","arxiv_id":"2411.06100","repositories_listed":0,"syntology":null},{"url":null,"slug":"gci-vital-gradual-confidence-improvement-with","title":"GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise","date":"2024-11-08","arxiv_id":"2411.05939","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-network-fragmentation-a-useful-complexity","title":"Is network fragmentation a useful complexity measure?","date":"2024-11-07","arxiv_id":"2411.04695","repositories_listed":0,"syntology":null},{"url":null,"slug":"saliency-assisted-quantization-for-neural","title":"Saliency Assisted Quantization for Neural Networks","date":"2024-11-07","arxiv_id":"2411.05858","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-shot-temporal-resolution-domain","title":"Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networks","date":"2024-11-07","arxiv_id":"2411.04760","repositories_listed":0,"syntology":null},{"url":null,"slug":"deferred-poisoning-making-the-model-more","title":"Deferred Poisoning: Making the Model More Vulnerable via Hessian Singularization","date":"2024-11-06","arxiv_id":"2411.03752","repositories_listed":0,"syntology":null},{"url":null,"slug":"overcoming-label-shift-in-targeted-federated","title":"Overcoming label shift in targeted federated learning","date":"2024-11-06","arxiv_id":"2411.03799","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-expansion-and-boundary-growth-for-open","title":"Domain Expansion and Boundary Growth for Open-Set Single-Source Domain Generalization","date":"2024-11-05","arxiv_id":"2411.02920","repositories_listed":0,"syntology":null},{"url":null,"slug":"judge-like-a-real-doctor-dual-teacher-sample","title":"Judge Like a Real Doctor: Dual Teacher Sample Consistency Framework for Semi-supervised Medical Image Classification","date":"2024-11-05","arxiv_id":"2411.03041","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-contextual-uncertainty-of-visual","title":"Exploiting Contextual Uncertainty of Visual Data for Efficient Training of Deep Models","date":"2024-11-04","arxiv_id":"2411.01925","repositories_listed":0,"syntology":null},{"url":null,"slug":"fusecaps-investigating-feature-fusion-based","title":"FUSECAPS: Investigating Feature Fusion Based Framework for Capsule Endoscopy Image Classification","date":"2024-11-04","arxiv_id":"2411.02637","repositories_listed":0,"syntology":null},{"url":null,"slug":"tripletclip-improving-compositional-reasoning","title":"TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives","date":"2024-11-04","arxiv_id":"2411.02545","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-deep-learning-infrastructures-for","title":"Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision","date":"2024-11-03","arxiv_id":"2411.01431","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-gastrointestinal-diagnostics-a-cnn","title":"Optimizing Gastrointestinal Diagnostics: A CNN-Based Model for VCE Image Classification","date":"2024-11-03","arxiv_id":"2411.01652","repositories_listed":0,"syntology":null},{"url":null,"slug":"parsecaps-an-interpretable-parsing-capsule","title":"ParseCaps: An Interpretable Parsing Capsule Network for Medical Image Diagnosis","date":"2024-11-03","arxiv_id":"2411.01564","repositories_listed":0,"syntology":null},{"url":null,"slug":"undermining-image-and-text-classification","title":"Undermining Image and Text Classification Algorithms Using Adversarial Attacks","date":"2024-11-03","arxiv_id":"2411.03348","repositories_listed":0,"syntology":null},{"url":null,"slug":"mic-medical-image-classification-using-chest","title":"MIC: Medical Image Classification Using Chest X-ray (COVID-19 and Pneumonia) Dataset with the Help of CNN and Customized CNN","date":"2024-11-02","arxiv_id":"2411.01163","repositories_listed":0,"syntology":null},{"url":null,"slug":"class-incremental-learning-with-task-specific","title":"Class Incremental Learning with Task-Specific Batch Normalization and Out-of-Distribution Detection","date":"2024-11-01","arxiv_id":"2411.00430","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-many-classifiers-do-we-need","title":"How many classifiers do we need?","date":"2024-11-01","arxiv_id":"2411.00328","repositories_listed":0,"syntology":null},{"url":null,"slug":"retrieval-enriched-zero-shot-image","title":"Retrieval-enriched zero-shot image classification in low-resource domains","date":"2024-11-01","arxiv_id":"2411.00988","repositories_listed":0,"syntology":null},{"url":null,"slug":"aerial-flood-scene-classification-using-fine","title":"Aerial Flood Scene Classification Using Fine-Tuned Attention-based Architecture for Flood-Prone Countries in South Asia","date":"2024-10-31","arxiv_id":"2411.00169","repositories_listed":0,"syntology":null},{"url":null,"slug":"cliperase-efficient-unlearning-of-visual","title":"CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP","date":"2024-10-30","arxiv_id":"2410.23330","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-decomposed-image-classification","title":"Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks","date":"2024-10-30","arxiv_id":"2410.23359","repositories_listed":0,"syntology":null},{"url":null,"slug":"active-learning-for-vision-language-models","title":"Active Learning for Vision-Language Models","date":"2024-10-29","arxiv_id":"2410.22187","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-optimization-for-hyperparameters","title":"Bayesian Optimization for Hyperparameters Tuning in Neural Networks","date":"2024-10-29","arxiv_id":"2410.21886","repositories_listed":0,"syntology":null},{"url":null,"slug":"developing-convolutional-neural-networks","title":"Developing Convolutional Neural Networks using a Novel Lamarckian Co-Evolutionary Algorithm","date":"2024-10-29","arxiv_id":"2410.22487","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficientnet-with-hybrid-attention-mechanisms","title":"Breast Cancer Histopathology Classification using CBAM-EfficientNetV2 with Transfer Learning","date":"2024-10-29","arxiv_id":"2410.22392","repositories_listed":0,"syntology":null},{"url":null,"slug":"fakeformer-efficient-vulnerability-driven","title":"FakeFormer: Efficient Vulnerability-Driven Transformers for Generalisable Deepfake Detection","date":"2024-10-29","arxiv_id":"2410.21964","repositories_listed":0,"syntology":null},{"url":null,"slug":"saliency-based-diversity-and-fairness-metric","title":"Saliency-Based diversity and fairness Metric and FaceKeepOriginalAugment: A Novel Approach for Enhancing Fairness and Diversity","date":"2024-10-29","arxiv_id":"2411.00831","repositories_listed":0,"syntology":null},{"url":null,"slug":"historical-test-time-prompt-tuning-for-vision","title":"Historical Test-time Prompt Tuning for Vision Foundation Models","date":"2024-10-27","arxiv_id":"2410.20346","repositories_listed":0,"syntology":null},{"url":null,"slug":"annotation-efficiency-identifying-hard","title":"Annotation Efficiency: Identifying Hard Samples via Blocked Sparse Linear Bandits","date":"2024-10-26","arxiv_id":"2410.20041","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-cnn-classification-with-lamarckian","title":"Enhancing CNN Classification with Lamarckian Memetic Algorithms and Local Search","date":"2024-10-26","arxiv_id":"2410.20234","repositories_listed":0,"syntology":null},{"url":null,"slug":"oreole-fm-successes-and-challenges-toward","title":"OReole-FM: successes and challenges toward billion-parameter foundation models for high-resolution satellite imagery","date":"2024-10-25","arxiv_id":"2410.19965","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-combinatorial-approach-to-neural-emergent","title":"A Combinatorial Approach to Neural Emergent Communication","date":"2024-10-24","arxiv_id":"2410.18806","repositories_listed":0,"syntology":null}],"record_sha256":"2686606fa141bbe5cffd9d7660d84b37a845dfb26d3de40ad0c9e27d5738cc4f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}