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Found 6 papers, 6 papers with code
Image and AIS Data Fusion Technique for Maritime Computer Vision Applications
This demonstrates the potential of our approach in creating datasets for vessel detection, pose estimation and auto-labelling pipelines.
Towards Realistic Evaluation of Industrial Continual Learning Scenarios with an Emphasis on Energy Consumption and Computational Footprint
This paper addresses research gaps between current IL research and industrial project environments, including varying incremental tasks and the introduction of Joint Training in tandem with IL.
Generating Annotated Training Data for 6D Object Pose Estimation in Operational Environments with Minimal User Interaction
Here, we present a proof of concept for a novel approach of autonomously generating annotated training data for 6D object pose estimation.
InterpretML: A Unified Framework for Machine Learning Interpretability
InterpretML is an open-source Python package which exposes machine learning interpretability algorithms to practitioners and researchers.
Axiomatic Interpretability for Multiclass Additive Models
In the first part of this paper, we generalize a state-of-the-art GAM learning algorithm based on boosted trees to the multiclass setting, and show that this multiclass algorithm outperforms existing GAM learning algorithms and sometimes matches the performance of full complexity models such as gradient boosted trees.
Considerations When Learning Additive Explanations for Black-Box Models
In this paper, we study global additive explanations for non-additive models, focusing on four explanation methods: partial dependence, Shapley explanations adapted to a global setting, distilled additive explanations, and gradient-based explanations.
