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Found 12 papers, 9 papers with code
A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting
Time Series Forecasting has been an active area of research due to its many applications ranging from network usage prediction, resource allocation, anomaly detection, and predictive maintenance.
SemSegLoss: A python package of loss functions for semantic segmentation
Image Segmentation has been an active field of research as it has a wide range of applications, ranging from automated disease detection to self-driving cars.
COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach
Our experimental results showcased that we can implement an efficient and accurate deep learning model for COVID-19 detection by adopting the few-shot learning approaches even with less amount of data.
Challenges and approaches to time-series forecasting in data center telemetry: A Survey
We hope that this evaluation provides a comprehensive summary to innovate in forecasting approaches for telemetry data.
An Overview of Deep Learning Architectures in Few-Shot Learning Domain
In this paper, we have reviewed some of the well-known deep learning-based approaches towards few-shot learning.
A survey of loss functions for semantic segmentation
In this paper, we have summarized some of the well-known loss functions widely used for Image Segmentation and listed out the cases where their usage can help in fast and better convergence of a model.
A comparative study of 2D image segmentation algorithms for traumatic brain lesions using CT data from the ProTECTIII multicenter clinical trial
The goal of segmentation is to increase efficiency and simplicity of visualization and quantification of regions of interest within a medical image.
SSM-Net for Plants Disease Identification in Low Data Regime
In this paper, we propose a new metrics-based few-shot learning SSM net architecture, which consists of stacked siamese and matching network components to address the problem of disease detection in low data regimes.
Schema Matching using Machine Learning
Schema Matching is a method of finding attributes that are either similar to each other linguistically or represent the same information.
Improving Siamese Networks for One Shot Learning using Kernel Based Activation functions
The lack of a large amount of training data has always been the constraining factor in solving a lot of problems in machine learning, making One Shot Learning one of the most intriguing ideas in machine learning.
Unsupervised video summarization framework using keyframe extraction and video skimming
Video is one of the robust sources of information and the consumption of online and offline videos has reached an unprecedented level in the last few years.
Military Simulator - A Case Study of Behaviour Tree and Unity based architecture
In this paper we show how the combination of Behaviour Tree and Utility Based AI architecture can be used to design more realistic bots for Military Simulators.
