• Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
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Artificial Neural Networks and Machine Learning – ICANN 2024 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part II

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.09.2024

Abbildungen

XXXIV, 464 p. 145 illus., 141 illus. in color.

Herausgeber

Michael Wand + weitere

Verlag

Springer

Seitenzahl

464

Maße (L/B/H)

23,5/15,5/2,7 cm

Gewicht

750 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72334-6

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

17.09.2024

Abbildungen

XXXIV, 464 p. 145 illus., 141 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

464

Maße (L/B/H)

23,5/15,5/2,7 cm

Gewicht

750 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-72334-6

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2024


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    .- An Energy Sampling Replay-Based Continual Learning Framework.



    .- Coarse-to-Fine Granularity in MultiScale FeatureFusion Network for SAR Ship Classification.



    .-Multi-scale convolutional attention fuzzy broad network for few-shot hyperspectral image classification.



    .- Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification.



    .- Computer Vision: Object Detection.



    .- CIA-Net:Cross-modal Interaction and Depth Quality-Aware Network for RGB-D Salient Object Detection.



    .- CPH DETR: Comprehensive Regression Loss for End-to-End Object Detection.



    .- DecoratingFusion: A LiDAR-Camera Fusion Network with the Combination of Point-level and Feature-level Fusion.



    .- EMDFNet: Efficient Multi-scale and Diverse Feature Network for Traffic Sign Detection.



    .- Global-Guided Weighted Enhancement for Salient Object Detection.



    .- KDNet: Leveraging Vision-Language Knowledge Distillation for Few-Shot Object Detection.



    .- MUFASA: Multi-View Fusion and Adaptation Network with Spatial Awareness for Radar Object Detection.



    .- One-Shot Object Detection with 4D-Correlation and 4D-Attention.



    .- Small Object Detection Based on Bidirectional Feature Fusion and Multi-scale Distillation.



    .-SRA-YOLO: Spatial Resolution Adaptive YOLO for Semi-Supervised Cross-Domain Aerial Object Detection.



    .- Computer Vision: Security and Adversarial Attacks.



    .- BiFAT: Bilateral Filtering and Attention Mechanisms in a Two-Stream Model for Deepfake Detection.



    .- EL-FDL: Improving Image Forgery Detection and Localization via Ensemble Learning.



    .- Generalizable Deepfake Detection with Unbiased Feature Extraction and Low-level Forgery Enhancement.



    .- Generative Universal Nullifying Perturbation for Countering Deepfakes through Combined Unsupervised Feature Aggregation.



    .- Noise-NeRF: Hide Information in Neural Radiance Field using Trainable Noise.



    .- Unconventional Face Adversarial Attack.



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    .- Computer Vision: Image Enhancement.



    .- A Study in Dataset Pruning for Image Super-Resolution.



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