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Data Impressions: Mining Deep Models to Extract Samples for Data-free Applications
Pretrained deep models hold their learnt knowledge in the form of the mo...
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Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation
Knowledge Distillation is an effective method to transfer the learning a...
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Dataset Condensation with Gradient Matching
Efficient training of deep neural networks is an increasingly important ...
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Adversarial Fooling Beyond "Flipping the Label"
Recent advancements in CNNs have shown remarkable achievements in variou...
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iDLG: Improved Deep Leakage from Gradients
It is widely believed that sharing gradients will not leak private train...
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Zero-Shot Knowledge Distillation in Deep Networks
Knowledge distillation deals with the problem of training a smaller mode...
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Gray-box Adversarial Training
Adversarial samples are perturbed inputs crafted to mislead the machine ...
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Ask, Acquire, and Attack: Data-free UAP Generation using Class Impressions
Deep learning models are susceptible to input specific noise, called adv...
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Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations
Machine learning models are susceptible to adversarial perturbations: sm...
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NAG: Network for Adversary Generation
Adversarial perturbations can pose a serious threat for deploying machin...
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CNN Fixations: An unraveling approach to visualize the discriminative image regions
Deep convolutional neural networks (CNN) have revolutionized various fie...
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Fast Feature Fool: A data independent approach to universal adversarial perturbations
State-of-the-art object recognition Convolutional Neural Networks (CNNs)...
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Deep image representations using caption generators
Deep learning exploits large volumes of labeled data to learn powerful m...
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A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Traditional architectures for solving computer vision problems and the d...
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Object Level Deep Feature Pooling for Compact Image Representation
Convolutional Neural Network (CNN) features have been successfully emplo...
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