Learn Before
When a Driving Pipeline Lacks the Right Information
In a modular self-driving system, car detection, pedestrian detection, and path planning can each perform well on their own inputs, yet the full system may still do poorly on camera scenes. That usually means the pipeline is missing a useful cue. Adding a lane-line detector can supply that missing road-context information so the planner does not need to interpret raw camera images directly.
0
1
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
A Multi-Stage Speech Recognition System
When a Driving Pipeline Lacks the Right Information
Handcrafted Stages Can Cap Overall System Quality
Domain-Designed Features Can Lower the Amount of Training Data Needed
Choosing Steps and Connections in a Pipeline
Choose Pipeline Stages You Can Train
Learned and Non-Learned Pipeline Components
If a hospital intake system has highly accurate symptom and risk classifiers, its end-to-end patient routing must also be highly accurate.
A multi-stage system works only if you can _____ enough labeled examples for each component.
Three Ideas in a Machine Learning Pipeline
How to Rework a Multi-Stage Machine Learning Pipeline
When a Multi-Stage Pipeline Hides Missing Information
Case Study: Diagnosing a Parcel-Delivery Drone Pipeline
Non-Learning Components in a Robotics Pipeline
When Does a Multi-Stage System Make Sense?
Pipelines Can Include Branching Steps
Learn After
A Modular Self-Driving System Breaks Driving Into Easier Tasks
Why can a self-driving system perform much worse than a human driver even if each module seems strong on its own?
True or False: In a delivery robot system, every component, including route selection, must be implemented as a learned model.
Adding a _____ detector to a bridge inspection workflow can give the maintenance team damage-location information without requiring them to inspect raw drone photos.
Which three parts make up the simple warehouse-robot navigation pipeline described here?
True or False: Every stage in a machine-learning system must be learned from data.
A warehouse navigation system underperforms overall even though localization, mapping, and obstacle detection are near human-level. Adding a _____ detector can provide the missing information.
Match each warehouse robot pipeline component to its role in the pipeline.
Arrange the steps for diagnosing a multi-stage document scanning system that lags behind people even though each module seems strong.
Why a Strong Set of Modules Can Still Produce Weak End-to-End Results
True or False: In this setting, collecting labeled data for road-sign and vehicle detectors is relatively easy because public datasets and crowdsourcing can supply many examples.
Scaling Labels With Distributed Human Annotation
Match each robotics pipeline diagnosis to its correct description.
Order the steps for fixing a missing-information problem in a warehouse robot system by adding a pallet detector.
Explain why a navigation stack can fail even when its parts look strong.
Find the bottleneck in a warehouse navigation pipeline.
Explain why adding a detector can simplify a processing pipeline.