Papers Data Augmentation
“Data Augmentation” 태그가 달린 논문 9,656편 · 필터 해제
Det-LIME: Detector-Aware, Multi-Instance Local Interpretable Model-Agnostic Explanations for Automated Marine Mammal Detection
Despite the rapid uptake of black-box object detectors in marine mammal research and monitoring, explainability techniques are rarely integrated into conservation workflows. Furthermore, most classification-oriented expl…
Data AugmentationIsotropic Embedding Perturbations for Robust Vision Language Encoders
Data augmentation is fundamental to training modern deep vision and multimodal models. While individual methods, such as RandAug, CutMix, Mixup, RandErase, and DropPath, offer strong regularization effects, their combine…
Data AugmentationHessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials
While machine-learning interatomic potentials (MLIPs) have successfully learned potential energy surfaces (PES) and atomic forces, many practical applications, such as vibrational analysis and transition state search, re…
Data AugmentationREER-PT: Reverse-Engineered Reasoning for Perplexity-Guided Pre-training Data Augmentation
As language-model compute continues to scale, high-quality training data is becoming an increasingly important bottleneck. Conventional next-token prediction supervises what follows a context but leaves the intermediate …
Data AugmentationAQ3D: Adaptive Query Transformer for 3D Instance Segmentation
Transformer-based decoders for 3D instance segmentation typically commit to a fixed number of queries and positional modeling calibrated on the training distribution rather than on the scene at hand. Indoor scans vary wi…
3D Instance SegmentationData AugmentationCross-Spectral Dense Correspondence for Multimodal Spectral Medical Imaging
Precise dense correspondence is a fundamental prerequisite for multimodal spectral imaging systems that fuse disparate wavelength ranges for subsequent analysis in medical and scientific imaging. Corresponding image poin…
Data AugmentationPhysics-Guided Flow Matching for CT Image Reconstruction
Deep generative models have recently emerged as powerful priors for solving ill-posed inverse problems in CT, with diffusion-based approaches achieving state-of-the-art reconstruction performance. However, diffusion mode…
Computational EfficiencyImage ReconstructionData AugmentationSelf-Augmented Diffusion Guidance for Physics-Informed Generation
Diffusion models can be used to generate spatiotemporal signals of physical phenomena, such as time-series images of fluid dynamics. However, a major limitation of standard diffusion models is that they do not incorporat…
Data AugmentationDifficulty-Aware Sample Allocation for Adaptive Data Augmentation in Semantic Segmentation
Data augmentation is a standard component of modern semantic segmentation pipelines, but most augmentation techniques allocate transformations uniformly across training samples or adapt to a single difficulty signal such…
Semantic SegmentationData AugmentationReal-World Knowledge-Guided Change Data Synthesis for Remote Sensing
Change data synthesis provides a cost-effective solution for expanding training data and improving the performance of change detection models. However, existing synthesis methods typically rely on handcrafted rules to si…
Data AugmentationChange DetectionBee Detection and Tracking at Hive Entrance using YOLO11 and ByteTrack
This work presents an automatic bee entrance monitoring system based on YOLO11 transfer learning and the ByteTrack tracking algorithm. The study investigates the influence of data augmentation, backbone freezing, and tra…
Data AugmentationTransfer LearningTransfer Learning in Nonparametric Regression with Deep ReLU Networks
This paper develops a general transfer learning framework for nonparametric regression with data consisting of multiple groups. Under the assumption that groups share a common structure along with group-specific deviatio…
Transfer LearningData AugmentationSAGE-XGBoost: Spatially Augmented Graph Embeddings--Machine Learning Framework for Natural Hazards Susceptibility Mapping under Data Scarcity
Natural hazard susceptibility mapping is often constrained by limited labeled data, reducing the generalizability of conventional machine learning and limiting the applicability of complex deep learning models. This stud…
Representation LearningFeature ImportanceData AugmentationGS-VLA: Plug-and-Play Viewpoint Canonicalization for Frozen VLA Policies via Gaussian Splatting
This paper proposes a lightweight, plug-and-play framework that improves robustness to viewpoint shifts in Vision-Language-Action (VLA) policies without policy retraining. To our knowledge, this is the first approach to …
Data AugmentationGeneralized Audio-Driven Synthesis of Precise Drummer Motion
Music-driven character animation enables and enhances transformative applications in entertainment and interactive education. However, synthesizing realistic drumming motion from audio remains challenging due to the inhe…
Data AugmentationSimple, Safe, and Overlooked: Reclaiming Sustainable Domain Generalization with Statistical Color Matching
Hardware shifts, color variations, and changing patient characteristics between development and deployment routinely break trained medical image classifiers. Existing remedies fall short: standard color jittering provide…
Domain GeneralizationData AugmentationStyle TransferLearning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets
Small-scale image classification is often limited by the scarcity of training data. Generative data augmentation (GDA) based on pretrained generative models has emerged as an effective solution. However, existing methods…
Image ClassificationData AugmentationThe Impact of CutMix on Reliability and Robustness in Semantic Segmentation
Ensuring not only high accuracy but also reliable and robust predictions is critical for the deployment of semantic segmentation models in safety-critical applications such as autonomous driving. Despite the widespread u…
Semantic SegmentationAutonomous DrivingData AugmentationAslema at NADI 2026: Augmentation through Fewshot for SLU
We present Aslema, our system for NADI 2026 Shared Task 5, which consists of two subtasks: intent recognition and slot filling. We evaluate four omni LLMs in a zero-shot setting and compare them with fine-tuned models. O…
Intent RecognitionData AugmentationSlot FillingSynthetic Data Augmentation for Satellite-Based Analysis of Battle-Damaged Agricultural Fields in Ukraine
Monitoring war-induced damage to agricultural land in Ukraine is important for understanding threats to food security, environmental stability, and post-war recovery. However, the development of computer-vision systems f…
Data Augmentation