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  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2021
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2021
Band 12892

Artificial Neural Networks and Machine Learning – ICANN 2021 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part II

Aus der Reihe Theoretical Computer Science and General Issues

98,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

11.09.2021

Abbildungen

XXIII, 651 p. 229 illus., 219 illus. in color.

Herausgeber

Igor Farkaš + weitere

Verlag

Springer

Seitenzahl

651

Maße (L/B/H)

23,5/15,5/3,7 cm

Gewicht

1007 g

Auflage

1st ed. 2021

Sprache

Englisch

ISBN

978-3-030-86339-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

11.09.2021

Abbildungen

XXIII, 651 p. 229 illus., 219 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

651

Maße (L/B/H)

23,5/15,5/3,7 cm

Gewicht

1007 g

Auflage

1st ed. 2021

Sprache

Englisch

ISBN

978-3-030-86339-5

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: ProductSafety@springernature.com

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  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2021
  • Produktbild: Artificial Neural Networks and Machine Learning – ICANN 2021

  • Computer vision and object detection.- Selective Multi-Scale Learning for Object Detection.- DRENet: Giving Full Scope to Detection and Regression-based Estimation for Video Crowd Counting.- Sisfrutos Papaya: a Dataset for Detection and Classification of Diseases in Papaya.- Faster-LTN: a neuro-symbolic, end-to-end object detection architecture.- GC-MRNet: Gated Cascade Multi-stage Regression Network for Crowd Counting.- Latent Feature-Aware and Local Structure-Preserving Network for 3D Completion from a single depth view.- Facial Expression Recognition by Expression-Specific Representation Swapping.- Iterative Error Removal for Time-of-Flight Depth Imaging.- Blurred Image Recognition: A Joint Motion Deblurring and Classification Loss-Aware Approach.- Learning How to Zoom in: Weakly Supervised ROI-based-DAM for Fine-Grained Visual Classification.- Convolutional neural networks and kernel methods.- ( Input) Size Matters for CNN Classifiers.- Accelerating Depthwise Separable Convolutions with Vector Processor.- KCNet: Kernel-based Canonicalization Network for entities in Recruitment Domain.- Deep Unitary Convolutional Neural Networks.- Deep learning and optimization I.- DPWTE: A Deep Learning Approach to Survival Analysis using a Parsimonious Mixture of Weibull Distributions.- First-order and second-order variants of the gradient descent in a unified framework.- Bayesian optimization for backpropagation in Monte-Carlo tree search.- Growing Neural Networks Achieve Flatter Minima.- Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks.- Curved SDE-Net Leads to Better Generalization for Uncertainty Estimates of DNNs.- EIS - Efficient and Trainable Activation Functions for Better Accuracy and Performance.- Deep learning and optimization II.- Why Mixup Improves the Model Performance.- Mixup gamblers: Learning to abstain with auto-calibrated reward for mixed samples.- Non-Iterative Phase Retrieval With Cascaded Neural Networks.- Incorporating Discrete Wavelet Transformation Decomposition Convolution into Deep Network to Achieve Light Training.- MMF: A loss extension for feature learning in open set recognition.- On the selection of loss functions under known weak label models.- Distributed and continual learning.- Bilevel Online Deep Learning in Non-stationary Environment.- A Blockchain Based Decentralized Gradient Aggregation Design for Federated Learning.- Continual Learning for Fake News Detection from Social Media.- Balanced Softmax Cross-Entropy for Incremental Learning.- Generalised Controller Design using Continual Learning.- DRILL: Dynamic Representations for Imbalanced Lifelong Learning.- Principal Gradient Direction and Confidence Reservoir Sampling for Continual Learning.- Explainable methods.- Spontaneous Symmetry Breaking in Data Visualization.- Deep NLP Explainer: Using Prediction Slope To Explain NLP Models.- Empirically explaining SGD from a line search perspective.- Towards Ontologically Explainable Classifiers.- Few-shot learning.- Leveraging the Feature Distribution in Transfer-based Few-Shot Learning.- One-Shot Meta-Learning for Radar-Based Gesture Sequences Recognition.- Few-Shot Learning With Random Erasing and Task-Relevant Feature Transforming.- Fostering Compositionality in Latent, Generative Encodings to Solve the Omniglot Challenge.- Better Few-shot Text Classification with Pre-trained Language Model.- Generative adversarial networks.- Leveraging GANs via Non-local Features.- On Mode Collapse in Generative Adversarial Networks.- Image Inpainting Using Wasserstein Generative Adversarial Imputation Network.- COViT-GAN: Vision Transformer for COVID-19 Detection in CT Scan Images with Self-Attention GAN for Data Augmentation.- PhonicsGAN: Synthesizing Graphical Videos from Phonics Songs.- A Progressive Image Inpainting Algorithm with a Mask Auto-update Branch.- Hybrid Generative Models for Two-Dimensional Datasets.- Towards Compressing Efficient Generative Adversarial Networks for Image Translation via Pruning and Distilling.-