Search Results for author:
Found 6 papers, 1 papers with code
Derm-T2IM: Harnessing Synthetic Skin Lesion Data via Stable Diffusion Models for Enhanced Skin Disease Classification using ViT and CNN
This study explores the utilization of Dermatoscopic synthetic data generated through stable diffusion models as a strategy for enhancing the robustness of machine learning model training.
A lightweight 3D dense facial landmark estimation model from position map data
As there is no public dataset available containing dense landmarks, we propose a pipeline to create a dense keypoint training dataset containing 520 key points across the whole face from an existing facial position map data.
Methodology for Building Synthetic Datasets with Virtual Humans
Creating a dataset that represents all variations of real-world faces is not feasible as the control over the quality of the data decreases with the size of the dataset.
Deep Reinforcement Learning: An Overview
In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern recognition, speech recognition, computer vision, and natural language processing.
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning
Recent advances in combining deep neural network architectures with reinforcement learning techniques have shown promising potential results in solving complex control problems with high dimensional state and action spaces.
Learning to predict where to look in interactive environments using deep recurrent q-learning
Bottom-Up (BU) saliency models do not perform well in complex interactive environments where humans are actively engaged in tasks (e. g., sandwich making and playing the video games).
