Landing Ai

Quinn Killough

Kai Yang

Andrew Yan-Tak Ng

Mark William Sabini

Daniel Bibireata

Sebastian Rodriguez

Yan Liu

Francisco Matias Cuenca-Acuna

Yu Qing Zhou

Pingyang He

Dillon Laird

Ankur Rawat

Shankaranand Jagadeesan

Yuxiang Zhang

Ramesh Sampath

Whitney Blodgett

Sanjay Boddhu

Gregory Diamos

Whit Blodgett

Sanjay Bodhu

Cristobal Silva

Juan Camilo Fernancez

Camilo Zapata

YuQing Zhou

Aldrin Leal

Junjie Guan

Mingrui Zhang

Zeqi Qiu

Camilo Iral

Leela Seshu Reddy Cheedepudi

Yazhou Cao

A model management system adaptively refines a training dataset for more effective visual inspection. The system trains a machine learning model using the initial training dataset and sends the trained model to a client for deployment. The deployment process generates outputs that are sent back to the system. The system determines that performance of predictions for noisy data points are inadequate and determines a cause of failure based on a mapping of the noisy data point to a distribution generated for the training dataset across multiple dimensions. The system determines a cause of failure based on an attribute of the noisy datapoint that deviates from the distribution of the training dataset and performs refinement towards the training dataset based on the identified cause of failure. The system retrains the machine learning model with the refined training dataset and sends the retrained machine learning model back to the client for re-deployment.

Model management system for improving training data through machine learning deployment

A model management system performs error analysis on results predicted by a machine learning model. The model management system identifies an incorrectly classified image outputted from a machine learning model and identifies using the Neural Template Matching (NTM) algorithm, an additional image correlated to the selected image. The system outputs correlated images based on a given image and a selection by a user through a user interface of a region of interest (ROI) of the given image. The region is defined by a bounding polygon input and the correlated images include features correlated to the features within the ROI. The system prompts a task associated with the additional image. The system receives a response that includes an indication that the additional image is incorrectly labeled and including a replacement label and instruct that the machine learning model be retrained using an updated training dataset that includes the replacement label.

Guided workflow for deep learning error analysis

Growing community of inventors

Sonoma, CA, United States of America

Quinn Killough