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Lieferstatus:	i.d.R. innert 5-10 Tagen versandfertig
Veröffentlichung:	Oktober 2022
Genre:	EDV / Informatik
	Applications / computerscience / ConferenceProceedings / Informatics / Research
ISBN:	9783031198021
EAN-Code:	9783031198021
Verlag:	Springer
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D 44 mm
Gewicht:	1212 gr
Seiten:	816
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

Active Label Correction Using Robust Parameter Update and Entropy Propagation.- Unpaired Image Translation via Vector Symbolic Architectures.- UniNet: Unified Architecture Search with Convolution, Transformer, and MLP.- AMixer: Adaptive Weight Mixing for Self-Attention Free Vision Transformers.- TinyViT: Fast Pretraining Distillation for Small Vision Transformers.- Equivariant Hypergraph Neural Networks.- ScaleNet: Searching for the Model to Scale.- Complementing Brightness Constancy with Deep Networks for Optical Flow Prediction.- ViTAS: Vision Transformer Architecture Search.- LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds.- Uncertainty-DTW for Time Series and Sequences.- Black-Box Few-Shot Knowledge Distillation.- Revisiting Batch Norm Initialization.- SSBNet: Improving Visual Recognition Efficiency by Adaptive Sampling.- Filter Pruning via Feature Discrimination in Deep Neural Networks.- LA3: Efficient Label-Aware AutoAugment.- Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps.- BA-Net: Bridge Attention for Deep Convolutional Neural Networks.- SAU: Smooth Activation Function Using Convolution with Approximate Identities.- Multi-Exit Semantic Segmentation Networks.- Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks.- PointScatter: Point Set Representation for Tubular Structure Extraction.- Check and Link: Pairwise Lesion Correspondence Guides Mammogram Mass Detection.- Graph-Constrained Contrastive Regularization for Semi-Weakly Volumetric Segmentation.- Generalizable Medical Image Segmentation via Random Amplitude Mixup and Domain-Specific Image Restoration.- Auto-FedRL: Federated Hyperparameter Optimization for Multi-Institutional Medical Image Segmentation.- Personalizing Federated Medical Image Segmentation via Local Calibration.- One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinement.- Ultra-High-Resolution Unpaired Stain Transformation via Kernelized Instance Normalization.- Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation.- ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images.- CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images.- UniMiSS: Universal Medical Self-Supervised Learning via Breaking Dimensionality Barrier.- DLME: Deep Local-Flatness Manifold Embedding.- Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching.- Graph Neural Network for Cell Tracking in Microscopy Videos.- CXR Segmentation by AdaIN-Based Domain Adaptation and Knowledge Distillation.- Accurate Detection of Proteins in Cryo-Electron Tomograms from Sparse Labels.- K-SALSA: K-Anonymous Synthetic Averaging of Retinal Images via Local Style Alignment.- RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-Guided Disease Classification.- Differentiable Zooming for Multiple Instance Learning on Whole-Slide Images.- Learning Uncoupled-Modulation CVAE for 3D Action-Conditioned Human Motion Synthesis.- Towards Grand Unification of Object Tracking.