Search Results for author:
Found 30 papers, 4 papers with code
Video Relationship Detection Using Mixture of Experts
Secondly, classifiers trained by a single, monolithic neural network often lack stability and generalization.
EPNet: An Efficient Pyramid Network for Enhanced Single-Image Super-Resolution with Reduced Computational Requirements
Single-image super-resolution (SISR) has seen significant advancements through the integration of deep learning.
Challenges for Predictive Modeling with Neural Network Techniques using Error-Prone Dietary Intake Data
In this work, we demonstrate the ways in which measurement error erodes the performance of neural networks, and illustrate the care that is required for leveraging these models in the presence of error.
Memory-Efficient Continual Learning Object Segmentation for Long Video
We propose two novel techniques to reduce the memory requirement of Online VOS methods while improving modeling accuracy and generalization on long videos.
A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset
In an effort to catalog insect biodiversity, we propose a new large dataset of hand-labelled insect images, the BIOSCAN-Insect Dataset.
Multi-Channel Feature Extraction for Virtual Histological Staining of Photon Absorption Remote Sensing Images
Experimental results reveal that specific combinations of features outperform the conventional channels by improving the labeling of tissue structures prior to model training.
Is Generative Modeling-based Stylization Necessary for Domain Adaptation in Regression Tasks?
Unsupervised domain adaptation (UDA) aims to bridge the gap between source and target domains in the absence of target domain labels using two main techniques: input-level alignment (such as generative modeling and stylization) and feature-level alignment (which matches the distribution of the feature maps, e. g. gradient reversal layers).
CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning
Continual learning in real-world scenarios is a major challenge.
Machine Learning Challenges of Biological Factors in Insect Image Data
The BIOSCAN project, led by the International Barcode of Life Consortium, seeks to study changes in biodiversity on a global scale.
Building Spatio-temporal Transformers for Egocentric 3D Pose Estimation
Egocentric 3D human pose estimation (HPE) from images is challenging due to severe self-occlusions and strong distortion introduced by the fish-eye view from the head mounted camera.
Virtual Histological Staining of Label-Free Total Absorption Photoacoustic Remote Sensing (TA-PARS)
We use a Pix2Pix generative adversarial network (GAN) to develop visualizations analogous to H&E staining from label-free TA-PARS images.
K-Means for Noise-Insensitive Multi-Dimensional Feature Learning
Many measurement modalities which perform imaging by probing an object pixel-by-pixel, such as via Photoacoustic Microscopy, produce a multi-dimensional feature (typically a time-domain signal) at each pixel.
Survey of Deep Learning Methods for Inverse Problems
In this paper we investigate a variety of deep learning strategies for solving inverse problems.
Deep Learning for Instance Retrieval: A Survey
In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics.
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes.
Improving Maximal Safe Brain Tumor Resection with Photoacoustic Remote Sensing Microscopy
Images obtained using this technique show comparable quality and contrast to the current standard for histopathological assessment of brain tissues.
Text Detection and Recognition in the Wild: A Review
Thus, unlike previous surveys in this field, the objectives of this survey are as follows: first, offering the reader not only a review on the recent advancement in scene text detection and recognition, but also presenting the results of conducting extensive experiments using a unified evaluation framework that assesses pre-trained models of the selected methods on challenging cases, and applies the same evaluation criteria on these techniques.
Deep Neural Network Perception Models and Robust Autonomous Driving Systems
This paper analyzes the robustness of deep learning models in autonomous driving applications and discusses the practical solutions to address that.
Potential adversarial samples for white-box attacks
Deep convolutional neural networks can be highly vulnerable to small perturbations of their inputs, potentially a major issue or limitation on system robustness when using deep networks as classifiers.
Assessing Architectural Similarity in Populations of Deep Neural Networks
Evolutionary deep intelligence has recently shown great promise for producing small, powerful deep neural network models via the synthesis of increasingly efficient architectures over successive generations.
Mitigating Architectural Mismatch During the Evolutionary Synthesis of Deep Neural Networks
Evolutionary deep intelligence has recently shown great promise for producing small, powerful deep neural network models via the organic synthesis of increasingly efficient architectures over successive generations.
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks
Generative Adversarial Networks (GANs) have shown considerable promise for mitigating the challenge of data scarcity when building machine learning-driven analysis algorithms.
Deep Learning for Generic Object Detection: A Survey
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images.
Texture Classification in Extreme Scale Variations using GANet
Because extreme scale variations are not necessarily present in most standard texture databases, to support the proposed extreme-scale aspects of texture understanding we are developing a new dataset, the Extreme Scale Variation Textures (ESVaT), to test the performance of our framework.
Nature vs. Nurture: The Role of Environmental Resources in Evolutionary Deep Intelligence
Evolutionary deep intelligence synthesizes highly efficient deep neural networks architectures over successive generations.
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
Texture is a fundamental characteristic of many types of images, and texture representation is one of the essential and challenging problems in computer vision and pattern recognition which has attracted extensive research attention.
The Mating Rituals of Deep Neural Networks: Learning Compact Feature Representations through Sexual Evolutionary Synthesis
Evolutionary deep intelligence was recently proposed as a method for achieving highly efficient deep neural network architectures over successive generations.
Domain Adaptation and Transfer Learning in StochasticNets
Transfer learning is a recent field of machine learning research that aims to resolve the challenge of dealing with insufficient training data in the domain of interest.
Efficient Deep Feature Learning and Extraction via StochasticNets
Experimental results show that features learned using deep convolutional StochasticNets, with fewer neural connections than conventional deep convolutional neural networks, can allow for better or comparable classification accuracy than conventional deep neural networks: relative test error decrease of ~4. 5% for classification on the STL-10 dataset and ~1% for classification on the SVHN dataset.
Forming A Random Field via Stochastic Cliques: From Random Graphs to Fully Connected Random Fields
However, the issue of computational tractability becomes a significant issue when incorporating such long-range nodal interactions, particularly when a large number of long-range nodal interactions (e. g., fully-connected random fields) are modeled.
