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Privacy-Preserving Near Neighbor Search via Sparse Coding with Ambiguation
In this paper, we propose a framework for privacy-preserving approximate...
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On Perfect Obfuscation: Local Information Geometry Analysis
We consider the problem of privacy-preserving data release for a specifi...
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Privacy-Preserving Image Sharing via Sparsifying Layers on Convolutional Groups
We propose a practical framework to address the problem of privacy-aware...
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Information bottleneck through variational glasses
Information bottleneck (IB) principle [1] has become an important elemen...
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ρ-VAE: Autoregressive parametrization of the VAE encoder
We make a minimal, but very effective alteration to the VAE model. This ...
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Single-Component Privacy Guarantees in Helper Data Systems and Sparse Coding
We investigate the privacy of two approaches to (biometric) template pro...
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Robustification of deep net classifiers by key based diversified aggregation with pre-filtering
In this paper, we address a problem of machine learning system vulnerabi...
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Reconstruction of Privacy-Sensitive Data from Protected Templates
In this paper, we address the problem of data reconstruction from privac...
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Defending against adversarial attacks by randomized diversification
The vulnerability of machine learning systems to adversarial attacks que...
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Clonability of anti-counterfeiting printable graphical codes: a machine learning approach
In recent years, printable graphical codes have attracted a lot of atten...
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Network Parameter Learning Using Nonlinear Transforms, Local Representation Goals and Local Propagation Constraints
In this paper, we introduce a novel concept for learning of the paramete...
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Clustering with Jointly Learned Nonlinear Transforms Over Discriminating Min-Max Similarity/Dissimilarity Assignment
This paper presents a novel clustering concept that is based on jointly ...
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Aggregation and Embedding for Group Membership Verification
This paper proposes a group membership verification protocol preventing ...
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Classification by Re-generation: Towards Classification Based on Variational Inference
As Deep Neural Networks (DNNs) are considered the state-of-the-art in ma...
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Bridging machine learning and cryptography in defence against adversarial attacks
In the last decade, deep learning algorithms have become very popular th...
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Privacy-Preserving Identification via Layered Sparse Code Design: Distributed Servers and Multiple Access Authorization
We propose a new computationally efficient privacy-preserving identifica...
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Network Learning with Local Propagation
This paper presents a locally decoupled network parameter learning with ...
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A multi-layer network based on Sparse Ternary Codes for universal vector compression
We present the multi-layer extension of the Sparse Ternary Codes (STC) f...
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Privacy Preserving Identification Using Sparse Approximation with Ambiguization
In this paper, we consider a privacy preserving encoding framework for i...
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A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising
A learning-based framework for representation of domain-specific images ...
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Sparse Ternary Codes for similarity search have higher coding gain than dense binary codes
This paper addresses the problem of Approximate Nearest Neighbor (ANN) s...
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