Search Results for author:
Found 15 papers, 1 papers with code
Saliency Diversified Deep Ensemble for Robustness to Adversaries
We empirically show a reduced transferability between ensemble members and improved performance compared to the state-of-the-art ensemble defense against medium and high strength white-box attacks.
Hyper-Representations: Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction
Self-Supervised Learning (SSL) has been shown to learn useful and information-preserving representations.
Zero-shot Voice Conversion via Self-supervised Prosody Representation Learning
We then use the learned prosodic representations as conditional information to train and enhance our VC model for zero-shot conversion.
Privacy-Preserving Near Neighbor Search via Sparse Coding with Ambiguation
In this paper, we propose a framework for privacy-preserving approximate near neighbor search via stochastic sparsifying encoding.
Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem Formulation
Due to the asynchronous nature, efficient learning of compact representation for event data is challenging.
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 parameters in a neural network.
Clustering with Jointly Learned Nonlinear Transforms Over Discriminating Min-Max Similarity/Dissimilarity Assignment
We introduce a clustering principle that is based on evaluation of a parametric min-max measure for the discriminative prior.
Network Learning with Local Propagation
This paper presents a locally decoupled network parameter learning with local propagation.
Learning non-linear transform with discriminative and minimum information loss priors
A novel measure related to the discriminative prior is proposed and defined on the support intersection for the transform representations.
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) for fast similarity search where we focus on the reconstruction of the database vectors from the ternary codes.
Privacy Preserving Identification Using Sparse Approximation with Ambiguization
The sparsifying transform and privacy amplification are not symmetric for the data owner and data user.
A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising
A learning-based framework for representation of domain-specific images is proposed where joint compression and denoising can be done using a VQ-based multi-layer network.
Regularized Residual Quantization: a multi-layer sparse dictionary learning approach
Furthermore, we also propose a general-purpose pre-processing for natural images which makes them suitable for such quantization.
Sparse Ternary Codes for similarity search have higher coding gain than dense binary codes
This paper addresses the problem of Approximate Nearest Neighbor (ANN) search in pattern recognition where feature vectors in a database are encoded as compact codes in order to speed-up the similarity search in large-scale databases.
Sparse Multi-layer Image Approximation: Facial Image Compression
We propose a scheme for multi-layer representation of images.
