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1. |
Record Nr. |
UNISA996466591803316 |
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Autore |
Väth Martin <1967-> |
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Titolo |
Ideal spaces / / Martin Vath |
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Pubbl/distr/stampa |
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Berlin, Germany : , : Springer-Verlag, , [1997] |
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©1997 |
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ISBN |
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Edizione |
[1st ed. 1997.] |
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Descrizione fisica |
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1 online resource (VI, 150 p.) |
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Collana |
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Lecture Notes in Mathematics ; ; 1664 |
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Classificazione |
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Disciplina |
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Soggetti |
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Ideal spaces |
Logic, Symbolic and mathematical |
Functional analysis |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di contenuto |
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Introduction -- Basic definitions and properties -- Ideal spaces with additional properties -- Ideal spaces on product measures and calculus -- Operators and applications -- Appendix: Some measurability results -- Sup-measurable operator functions -- Majorising principles for measurable operator functions -- A generalization of a theorem of Luxemburg-Gribanov -- References -- Index. |
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Sommario/riassunto |
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Ideal spaces are a very general class of normed spaces of measurable functions, which includes e.g. Lebesgue and Orlicz spaces. Their most important application is in functional analysis in the theory of (usual and partial) integral and integro-differential equations. The book is a rather complete and self-contained introduction into the general theory of ideal spaces. Some emphasis is put on spaces of vector-valued functions and on the constructive viewpoint of the theory (without the axiom of choice). The reader should have basic knowledge in functional analysis and measure theory. |
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2. |
Record Nr. |
UNISA996500066703316 |
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Titolo |
Computer vision - ECCV 2022 . Part V : 17th European Conference, Tel Aviv, Israel, October 23-27, 2022 : proceedings / / Shai Avidan [and four others] |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer, , [2022] |
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©2022 |
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ISBN |
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Descrizione fisica |
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1 online resource (804 pages) |
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Collana |
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Lecture Notes in Computer Science |
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Disciplina |
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Soggetti |
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Computer vision - Equipment and supplies |
Pattern recognition systems - Data processing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- Foreword -- Preface -- Organization -- Contents - Part V -- Adaptive Image Transformations for Transfer-Based Adversarial Attack -- 1 Introduction -- 2 Related Work -- 2.1 Adversarial Attack -- 2.2 Adversarial Defense -- 3 Method -- 3.1 Notations -- 3.2 Overview of AITL -- 3.3 Training AITL -- 3.4 Generating Adversarial Examples with AITL -- 4 Experiments -- 4.1 Settings -- 4.2 Compared with the State-of-the-Art Methods -- 4.3 Analysis on Image Transformation Operations -- 5 Conclusion -- References -- Generative Multiplane Images: Making a 2D GAN 3D-Aware -- 1 Introduction -- 2 Related Work and Background -- 3 Generative Multiplane Images (GMPI) -- 3.1 Overview -- 3.2 GMPI: StyleGANv2 with an Alpha Branch -- 3.3 Differentiable Rendering in GMPI -- 3.4 Discriminator Pose Conditioning -- 3.5 Miscellaneous Adjustment: Shading-Guided Training -- 3.6 Training -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Results -- 4.4 Ablation Studies -- 5 Conclusion -- References -- AdvDO: Realistic Adversarial Attacks for Trajectory Prediction -- 1 Introduction -- 2 Related Works -- 3 Problem Formulation and Challenges -- 4 AdvDO: Adversarial Dynamic Optimization -- 4.1 Dynamic Parameters Estimation -- 4.2 Adversarial Trajectory Generation -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Main Results -- 6 Conclusion -- References -- Adversarial |
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Contrastive Learning via Asymmetric InfoNCE -- 1 Introduction -- 2 Asymmetric InfoNCE -- 2.1 Notations -- 2.2 Asymmetric InfoNCE: A Generic Learning Objective -- 3 Adversarial Asymmetric Contrastive Learning -- 3.1 Adversarial Samples as Inferior Positives -- 3.2 Adversarial Samples as Hard Negatives -- 4 Experiments -- 4.1 Main Results -- 4.2 Transferring Robust Features -- 4.3 Ablation Studies -- 4.4 Qualitative Analysis -- 5 Related Work -- 6 Conclusions -- References. |
One Size Does NOT Fit All: Data-Adaptive Adversarial Training -- 1 Introduction -- 2 Related Work -- 2.1 Adversarial Defense -- 2.2 Decline of the Natural Accuracy -- 3 Review of Standard Adversarial Training -- 4 Adversarial Perturbation Size Matters -- 5 Data-Adaptive Adversarial Training -- 6 Experiment -- 6.1 Investigation of Hyper-parameters -- 6.2 White-Box and Black-Box Attacks -- 6.3 Distribution of Adversaries -- 6.4 DAAT with Different Calibration Strategies -- 7 Conclusions -- References -- UniCR: Universally Approximated Certified Robustness via Randomized Smoothing -- 1 Introduction -- 2 Universally Approximated Certified Robustness -- 2.1 Universal Certified Robustness -- 2.2 Approximating Tight Certified Robustness -- 3 Deriving Certified Radius Within Robustness Boundary -- 3.1 Calculating Certified Radius in Practice -- 4 Optimizing Noise PDF for Certified Robustness -- 4.1 Noise PDF Optimization -- 4.2 C-OPT and I-OPT -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Universality Evaluation -- 5.3 Optimizing Certified Radius with C-OPT -- 5.4 Optimizing Certified Radius with I-OPT -- 5.5 Best Performance Comparison -- 6 Related Work -- 7 Conclusion -- References -- Hardly Perceptible Trojan Attack Against Neural Networks with Bit Flips -- 1 Introduction -- 2 Related Works and Preliminaries -- 3 Methodology -- 3.1 Hardly Perceptible Trigger -- 3.2 Problem Formulation -- 3.3 Optimization -- 4 Experiments -- 4.1 Setup -- 4.2 Attack Results -- 4.3 Human Perceptual Study -- 4.4 Discussions -- 4.5 Potential Defenses -- 4.6 Ablation Studies -- 5 Conclusion -- References -- Robust Network Architecture Search via Feature Distortion Restraining -- 1 Introduction -- 2 Related Works -- 3 Preliminary -- 4 Method -- 4.1 Network Vulnerability Constraint -- 4.2 Robust Network Architecture Search (RNAS) -- 5 Experiment -- 5.1 Experimental Setup. |
5.2 Results on CIFAR-10 -- 5.3 Results on CIFAR-100, SVHN and Tiny-ImageNet -- 5.4 Upper Bound H of network vulnerability -- 6 Conclusion -- References -- SecretGen: Privacy Recovery on Pre-trained Models via Distribution Discrimination -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Method Overview -- 3.3 Generation Backbone of SecretGen -- 3.4 Pseudo Label Predictor -- 3.5 Latent Vector Selector -- 3.6 SecretGen Optimization -- 3.7 Discussion -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Evaluation Protocols -- 4.3 Attack Performance -- 4.4 Robustness Evaluation -- 4.5 Ablation Studies -- 5 Conclusion -- References -- Triangle Attack: A Query-Efficient Decision-Based Adversarial Attack -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Preliminaries -- 3.2 Motivation -- 3.3 Triangle Attack -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Evaluation on Standard Models -- 4.3 Evaluation on Real-world Applications -- 4.4 Ablation Study -- 4.5 Further Discussion -- 5 Conclusion -- References -- Data-Free Backdoor Removal Based on Channel Lipschitzness -- 1 Introduction -- 2 Related Work -- 2.1 Backdoor Attack -- 2.2 Backdoor Defense -- 3 Preliminaries -- 3.1 Notations -- 3.2 L-Lipschitz Function -- 3.3 Lipschitz Constant in Neural Networks -- 4 Methodology -- 4.1 Channel Lipschitz Constant -- 4.2 Trigger- |
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Activated Change -- 4.3 Correlation Between CLC and TAC -- 4.4 Special Case in CNN with Batch Normalization -- 4.5 Channel Lipschitzness Based Pruning -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 5.3 Ablation Studies -- 6 Conclusions -- References -- Black-Box Dissector: Towards Erasing-Based Hard-Label Model Stealing Attack -- 1 Introduction -- 2 Background and Notions -- 3 Method -- 3.1 A CAM-driven Erasing Strategy -- 3.2 A Random-Erasing-Based Self-KD Module. |
4 Experiments -- 4.1 Experiment Settings -- 4.2 Experiment Results -- 5 Conclusion -- References -- Learning Energy-Based Models with Adversarial Training -- 1 Introduction -- 2 Related Work -- 3 Background -- 3.1 Energy-Based Models -- 3.2 Binary Adversarial Training -- 4 Binary at Generative Model -- 4.1 Optimal Solution to the Binary at Problem -- 4.2 Learning Mechanism -- 4.3 Maximum Likelihood Learning Interpretation -- 4.4 Improved Training of Binary at Generative Model -- 5 Experiments -- 5.1 Image Generation -- 5.2 Applications -- 5.3 Training Stability Analysis -- 6 Conclusion -- References -- Adversarial Label Poisoning Attack on Graph Neural Networks via Label Propagation -- 1 Introduction -- 2 Preliminaries and Related Work -- 2.1 Graph Convolution Network and Its Decoupled Variants -- 2.2 Label Propagation and Its Role in GCN -- 2.3 Adversarial Poisoning Attacks on Graphs -- 3 Label Poisoning Attack Model -- 3.1 Label Propagation -- 3.2 Maximum Gradient Attack -- 3.3 GCN Training with Poisoned Labels -- 4 Experiments -- 5 Attack Performance Evaluation -- 6 Conclusion -- References -- Revisiting Outer Optimization in Adversarial Training -- 1 Introduction -- 2 Analyzing Outer Optimization in AT -- 2.1 Notations -- 2.2 Comparison of Gradient Properties -- 2.3 Revisiting Stochastic Gradient Descent -- 2.4 Example-Normalized Gradient Descent with Momentum -- 2.5 Accelerating ENGM via Gradient Norm Approximation -- 3 Experiments and Analysis -- 3.1 Comparison of Optimization Methods -- 3.2 Combination with Benchmark at Methods -- 3.3 Comparison with SOTA -- 3.4 Ablation Studies -- 4 Conclusion -- References -- Zero-Shot Attribute Attacks on Fine-Grained Recognition Models -- 1 Introduction -- 2 Related Works -- 3 Fine-Grained Compositional Adversarial Attacks -- 3.1 Problem Setting. |
3.2 Compositional Attribute-Based Universal Perturbations (CAUPs) -- 3.3 Learning AUPs -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 5 Conclusions -- References -- Towards Effective and Robust Neural Trojan Defenses via Input Filtering -- 1 Introduction -- 2 Standard Trojan Attack -- 3 Difficulty in Finding Input-Specific Triggers -- 4 Proposed Trojan Defenses -- 4.1 Variational Input Filtering -- 4.2 Adversarial Input Filtering -- 4.3 Filtering Then Contrasting -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Results of Baseline Defenses -- 5.3 Results of Proposed Defenses -- 5.4 Ablation Studies -- 6 Related Work -- 7 Conclusion -- References -- Scaling Adversarial Training to Large Perturbation Bounds -- 1 Introduction -- 2 Related Works -- 3 Preliminaries and Threat Model -- 3.1 Notation -- 3.2 Nomenclature of Adversarial Attacks -- 3.3 Objectives of the Proposed Defense -- 4 Proposed Method -- 5 Analysing Oracle Alignment of Adversarial Attacks -- 6 Role of Image Contrast in Robust Evaluation -- 7 Experiments and Results -- 8 Conclusions -- References -- Exploiting the Local Parabolic Landscapes of Adversarial Losses to Accelerate Black-Box Adversarial Attack -- 1 Introduction -- 1.1 Related Works -- 2 Background -- 3 Theoretical and Empirical Study on the Landscape of the Adversarial Loss -- 4 The BABIES Algorithm -- 5 Experimental Evaluation -- 5.1 Results of BABIES on MNIST, CIFAR-10 and ImageNet |
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-- 5.2 Results on Attacking Google Cloud Vision -- 6 Discussion and Conclusion -- References -- Generative Domain Adaptation for Face Anti-Spoofing -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overview -- 3.2 Generative Domain Adaptation -- 3.3 Overall Objective and Optimization -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Comparisons to the State-of-the-Art Methods -- 4.3 Ablation Studies. |
4.4 Visualization and Analysis. |
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