Papers Robust classification
“Robust classification” 태그가 달린 논문 295편 · 필터 해제
SCISSOR: Mitigating Semantic Bias through Cluster-Aware Siamese Networks for Robust Classification
Shortcut learning undermines model generalization to out-of-distribution data. While the literature attributes shortcuts to biases in superficial features, we show that imbalances in the semantic distribution of sample e…
Data AugmentationRobust classificationQuantum-Informed Contrastive Learning with Dynamic Mixup Augmentation for Class-Imbalanced Expert Systems
Expert systems often operate in domains characterized by class-imbalanced tabular data, where detecting rare but critical instances is essential for safety and reliability. While conventional approaches, such as cost-sen…
Contrastive LearningRobust classificationTripletCEM-FBGTinyDet: Context-Enhanced Foreground Balance with Gradient Tuning for tiny Objects
Tiny object detection (TOD) reveals a fundamental flaw in feature pyramid networks: high-level features (P5-P6) frequently receive zero positive anchors under standard label assignment protocols, leaving their semantic r…
object-detectionObject DetectionRobust classificationInherently Faithful Attention Maps for Vision Transformers
We introduce an attention-based method that uses learned binary attention masks to ensure that only attended image regions influence the prediction. Context can strongly affect object perception, sometimes leading to bia…
ClassificationRobust classificationIdentifying and Understanding Cross-Class Features in Adversarial Training
Adversarial training (AT) has been considered one of the most effective methods for making deep neural networks robust against adversarial attacks, while the training mechanisms and dynamics of AT remain open research pr…
Robust classificationUncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout
INTRODUCTION: Alzheimer's disease (AD) is genetically complex, complicating robust classification from genomic data. METHODS: We developed a transformer-based ensemble model (TrUE-Net) using Monte Carlo Dropout for uncer…
ClassificationRobust classificationAcoustic Classification of Maritime Vessels using Learnable Filterbanks
Reliably monitoring and recognizing maritime vessels based on acoustic signatures is complicated by the variability of different recording scenarios. A robust classification framework must be able to generalize across di…
ClassificationRobust classificationCNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical Data
The COVID-19 pandemic, caused by SARS-CoV-2, highlighted the critical need for accurate prediction of disease severity to optimize healthcare resource allocation and patient management. The spike protein, which facilitat…
Feature EngineeringRobust classificationseverity predictionRespond to Change with Constancy: Instruction-tuning with LLM for Non-I.I.D. Network Traffic Classification
Encrypted traffic classification is highly challenging in network security due to the need for extracting robust features from content-agnostic traffic data. Existing approaches face critical issues: (i) Distribution dri…
Robust classificationTraffic ClassificationMachine Learning Approaches to Vocal Register Classification in Contemporary Male Pop Music
For singers of all experience levels, one of the most daunting challenges in learning technical repertoire is navigating placement and vocal register in and around the passagio (passage between chest voice and head voice…
Robust classificationContrastive Normalizing Flows for Uncertainty-Aware Parameter Estimation
Estimating physical parameters from data is a crucial application of machine learning (ML) in the physical sciences. However, systematic uncertainties, such as detector miscalibration, induce data distribution distortion…
parameter estimationRobust classificationUncertainty QuantificationFine-Grained Bias Exploration and Mitigation for Group-Robust Classification
Achieving group-robust generalization in the presence of spurious correlations remains a significant challenge, particularly when bias annotations are unavailable. Recent studies on Class-Conditional Distribution Balanci…
Binary ClassificationRobust classificationSupervised and Unsupervised Textile Classification via Near-Infrared Hyperspectral Imaging and Deep Learning
Recycling textile fibers is critical to reducing the environmental impact of the textile industry. Hyperspectral near-infrared (NIR) imaging combined with advanced deep learning algorithms offers a promising solution for…
Deep LearningRobust classificationLow-dimensional representation of brain networks for seizure risk forecasting
Identifying preictal states -- periods during which seizures are more likely to occur -- remains a central challenge in clinical computational neuroscience. In this study, we introduce a novel framework that embeds funct…
EEGRobust classificationSemantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings
The rapid evolution of malware variants requires robust classification methods to enhance cybersecurity. While Large Language Models (LLMs) offer potential for generating malware descriptions to aid family classification…
AttributeMalware ClassificationMeta-LearningRobust classificationClassification of Firn Data via Topological Features
In this paper we evaluate the performance of topological features for generalizable and robust classification of firn image data, with the broader goal of understanding the advantages, pitfalls, and trade-offs in topolog…
ClassificationRobust classificationTopological Data AnalysisTowards Hardware Supported Domain Generalization in DNN-Based Edge Computing Devices for Health Monitoring
Deep neural network (DNN) models have shown remarkable success in many real-world scenarios, such as object detection and classification. Unfortunately, these models are not yet widely adopted in health monitoring due to…
Domain GeneralizationEdge-computingRobust classificationAI-Augmented Thyroid Scintigraphy for Robust Classification
Thyroid scintigraphy is a key imaging modality for diagnosing thyroid disorders. Deep learning models for thyroid scintigraphy classification often face challenges due to limited and imbalanced datasets, leading to subop…
ClassificationData Augmentationimage-classificationImage Classification+2HALO: Robust Out-of-Distribution Detection via Joint Optimisation
Effective out-of-distribution (OOD) detection is crucial for the safe deployment of machine learning models in real-world scenarios. However, recent work has shown that OOD detection methods are vulnerable to adversarial…
Out-of-Distribution DetectionOut of Distribution (OOD) DetectionRobust classificationAdversarial Training for Defense Against Label Poisoning Attacks
As machine learning models grow in complexity and increasingly rely on publicly sourced data, such as the human-annotated labels used in training large language models, they become more vulnerable to label poisoning atta…
Bilevel OptimizationRobust classification