IFT received an Air Force STTR Phase I award entitled “Machine Learning based Domain Adaptation (MLB-DA) for Multiple Source Classification and Fusion.”  IFT will develop a deep learning-based multi-source self-correcting approach to fuse data with different modalities at the data-level to maximize their capabilities to detect unanticipated events/targets. The proposed deep neural network approach holds great capability of adapting to changes of the input distribution allowing self-correcting multiple source classification and fusion. It is focused on learning features that combine (i) discriminativeness and (ii) domain-invariance. The classifiers can adapt to the target domain with different distribution without retraining new input data.