Search Results for author:
Found 16 papers, 6 papers with code
MRZ code extraction from visa and passport documents using convolutional neural networks
Detecting and extracting information from Machine-Readable Zone (MRZ) on passports and visas is becoming increasingly important for verifying document authenticity.
Optical Character Recognition
Optical Character Recognition (OCR)
OCR Graph Features for Manipulation Detection in Documents
Detecting manipulations in digital documents is becoming increasingly important for information verification purposes.
Optical Character Recognition
Optical Character Recognition (OCR)
NoPeek: Information leakage reduction to share activations in distributed deep learning
For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensitive raw data patterns across client communications while maintaining model accuracy.
Printing and Scanning Attack for Image Counter Forensics
Of the three models, our proposed model outperforms the others when trained and validated on images from a single printer.
Maximal adversarial perturbations for obfuscation: Hiding certain attributes while preserving rest
In this paper we investigate the usage of adversarial perturbations for the purpose of privacy from human perception and model (machine) based detection.
Detailed comparison of communication efficiency of split learning and federated learning
We compare communication efficiencies of two compelling distributed machine learning approaches of split learning and federated learning.
No Peek: A Survey of private distributed deep learning
We survey distributed deep learning models for training or inference without accessing raw data from clients.
Split learning for health: Distributed deep learning without sharing raw patient data
Can health entities collaboratively train deep learning models without sharing sensitive raw data?
Maximum-Entropy Fine Grained Classification
Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators to collect data.
Ranked #19 on
Fine-Grained Image Classification
on NABirds
(using extra training data)
Distributed learning of deep neural network over multiple agents
Our algorithm paves the way for distributed training of deep neural networks in data sensitive applications when raw data may not be shared directly.
Maximum-Entropy Fine-Grained Classification
Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators to collect data.
Accelerating Neural Architecture Search using Performance Prediction
Methods for neural network hyperparameter optimization and meta-modeling are computationally expensive due to the need to train a large number of model configurations.
Pairwise Confusion for Fine-Grained Visual Classification
Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity.
Ranked #16 on
Fine-Grained Image Classification
on Stanford Dogs
Designing Neural Network Architectures using Reinforcement Learning
We introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing CNN architectures for a given learning task.
Multi-velocity neural networks for gesture recognition in videos
We present a new action recognition deep neural network which adaptively learns the best action velocities in addition to the classification.
Deep video gesture recognition using illumination invariants
In this paper we present architectures based on deep neural nets for gesture recognition in videos, which are invariant to local scaling.
