Search Results for author:
Found 9 papers, 4 papers with code
Point2Mask: A Weakly Supervised Approach for Cell Segmentation Using Point Annotation
This paper presents a weakly supervised approach, which can perform cell instance segmentation by using only point and bounding box-based annotation.
Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey
The availability of representative datasets is an essential prerequisite for many successful artificial intelligence and machine learning models.
F2DNet: Fast Focal Detection Network for Pedestrian Detection
However, the current two-stage detectors are inefficient as they do bounding box regression in multiple steps i. e. in region proposal networks and bounding box heads.
Ranked #2 on
Pedestrian Detection
on Caltech
(using extra training data)
Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification
With the advent of machine learning in applications of critical infrastructure such as healthcare and energy, privacy is a growing concern in the minds of stakeholders.
DeepCeNS: An end-to-end Pipeline for Cell and Nucleus Segmentation in Microscopic Images
To address the aforementioned challenges, DeepCeNS is proposed in this paper to detect and segment cells and nucleus in microscopic images.
Ranked #1 on
Cell Segmentation
on EVICAN
XAI Handbook: Towards a Unified Framework for Explainable AI
The field of explainable AI (XAI) has quickly become a thriving and prolific community.
TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features
This indicates a vital gap between the explainability provided by the systems and the novice user.
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series
In contrast to the anomaly detection methods where anomalies are learned, DeepAnT uses unlabeled data to capture and learn the data distribution that is used to forecast the normal behavior of a time series.
TSViz: Demystification of Deep Learning Models for Time-Series Analysis
This is a step towards making informed/explainable decisions in the domain of time-series, powered by deep learning.
