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Found 10 papers, 1 papers with code
A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning
To address this we introduce a novel embedding constraint based on the Method of Moments (MoM).
RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget
Learning High-Resolution representations is essential for semantic segmentation.
Semi-supervised Contrastive Outlier removal for Pseudo Expectation Maximization (SCOPE)
Semi-supervised learning is the problem of training an accurate predictive model by combining a small labeled dataset with a presumably much larger unlabeled dataset.
Mitigating domain shift in AI-based tuberculosis screening with unsupervised domain adaptation
We show that without domain adaptation, ResNet-50 has difficulty in generalizing between imaging distributions from a number of public Tuberculosis screening datasets with imagery from geographically distributed regions.
CCS-GAN: COVID-19 CT-scan classification with very few positive training images
CCS-GAN achieves high accuracy with few positive images and thereby greatly reduces the barrier of acquiring large training volumes in order to train a diagnostic classifier for COVID-19.
Person Re-Identification with a Locally Aware Transformer
At present, the majority of Person re-ID techniques are based on Convolutional Neural Networks (CNNs), but Vision Transformers are beginning to displace pure CNNs for a variety of object recognition tasks.
Ranked #1 on
Person Re-Identification
on CUHK03
Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network
We present a novel conditional Generative Adversarial Network (cGAN) architecture that is capable of generating 3D Computed Tomography scans in voxels from noisy and/or pixelated approximations and with the potential to generate full synthetic 3D scan volumes.
Passive Encrypted IoT Device Fingerprinting with Persistent Homology
Buttechniques to fingerprint devices based on inter-packet arrival time (IAT) are an important area of research, as this feature is available even in encrypted traffic. We demonstrate that Topological Data Analysis (TDA) using persistent homology over IAT packet windows is a viable approach to obtain discriminative features for device fingerprinting.
Deep Expectation-Maximization for Semi-Supervised Lung Cancer Screening
We present a semi-supervised algorithm for lung cancer screening in which a 3D Convolutional Neural Network (CNN) is trained using the Expectation-Maximization (EM) meta-algorithm.
Generating Realistic COVID19 X-rays with a Mean Teacher + Transfer Learning GAN
We present a novel Mean Teacher + Transfer GAN (MTT-GAN) that generates COVID19 chest X-ray images of high quality.
