A survey on heterogeneous transfer learning
Transfer learning has been demonstrated to be effective for many real-world applications as
it exploits knowledge present in labeled training data from a source domain to enhance a …
it exploits knowledge present in labeled training data from a source domain to enhance a …
[HTML][HTML] A comprehensive survey on load forecasting hybrid models: Navigating the Futuristic demand response patterns through experts and intelligent systems
Load forecasting is a crucial task, which is carried out by utility companies for sake of power
grids' successful planning, optimized operation and control, enhanced performance, and …
grids' successful planning, optimized operation and control, enhanced performance, and …
Adaptive batch normalization for practical domain adaptation
Deep neural networks (DNN) have shown unprecedented success in various computer
vision applications such as image classification and object detection. However, it is still a …
vision applications such as image classification and object detection. However, it is still a …
Transfer learning with seasonal and trend adjustment for cross-building energy forecasting
Large scale smart meter deployments have resulted in popularization of sensor-based
electricity forecasting which relies on historical sensor data to infer future energy …
electricity forecasting which relies on historical sensor data to infer future energy …
Keypoint-guided optimal transport with applications in heterogeneous domain adaptation
Abstract Existing Optimal Transport (OT) methods mainly derive the optimal transport
plan/matching under the criterion of transport cost/distance minimization, which may cause …
plan/matching under the criterion of transport cost/distance minimization, which may cause …
Adversarial mixup ratio confusion for unsupervised domain adaptation
Multimedia applications often involve knowledge transfer across domains, eg, from images
to texts, where Unsupervised Domain Adaptation (UDA) can be used to reduce the domain …
to texts, where Unsupervised Domain Adaptation (UDA) can be used to reduce the domain …
Domain adaptation in small-scale and heterogeneous biological datasets
Machine-learning models are key to modern biology, yet models trained on one dataset are
often not generalizable to other datasets from different cohorts or laboratories due to both …
often not generalizable to other datasets from different cohorts or laboratories due to both …
Ozone concentration forecasting based on artificial intelligence techniques: a systematic review
The prediction of tropospheric ozone concentrations is vital due to ozone's passive impacts
on atmosphere, people's health, flora and fauna. However, ozone prediction is a complex …
on atmosphere, people's health, flora and fauna. However, ozone prediction is a complex …
TransLoc: A heterogeneous knowledge transfer framework for fingerprint-based indoor localization
Transfer learning algorithms (TLAs) are often used to solve the distribution discrepancy
issue in fingerprint-based indoor localization. However, existing TLAs cannot react well to …
issue in fingerprint-based indoor localization. However, existing TLAs cannot react well to …
Cross-domain structure preserving projection for heterogeneous domain adaptation
Abstract Heterogeneous Domain Adaptation (HDA) addresses the transfer learning
problems where data from the source and target domains are of different modalities (eg …
problems where data from the source and target domains are of different modalities (eg …