In recent years, artificial intelligence systems achieved state-of-the-art performances in a number of computer vision applications. For instance, in image classification, deep learning based neural networks surpassed top-5 human error rate of 5.1% on ImageNet dataset. However, these networks are susceptible to adversarial noise that, when added to the images on ImageNet, can cause an error rate of 100% with no discernible change in the images themselves. At OLIVES, we develop algorithms that can robustly operate under real-world challenging conditions through weakly supervised learning, backpropogated gradients, and transfer learning. We introduced three large-scale datasets (>1M) with controlled challenging conditions to test and develop robust algorithms: CURE-TSD,  CURE-TSR, CURE-OR.

1.Learning under Limited Data and Labels

a.A Gating Model for Bias Calibration in Generalized Zero-Shot Learning

b.Novel Paradigms under Label-Constrained Applications in subsurface imaging

c.Label-Constrained Applications in Autonomous Driving

d.Patient Aware Active Learning for Stable Medical Image Classification

e.Novel Paradigms under Label-Constrained Applications in X-ray imaging

f.Synthetic Data Generation for Seismic Self-Supervision

g.Synthetic Data Generation for self-supervision using challenging data  CURE-TSD/R

h.Contrastive Learning

i.Weakly Supervised Seismic Interpretation for Labeled Data Constrained Settings

j.Semi-supervised Sequence Modeling for Elastic Impedance Inversion

k.Joint Learning for Seismic Inversion

 

2.Learning under Adversaries

b.Stochastic Robust Trajectory Prediction