DeepAI AI Chat
Log In Sign Up

A Machine Learning Approach for Evaluating Creative Artifacts

by   Disha Shrivastava, et al.

Much work has been done in understanding human creativity and defining measures to evaluate creativity. This is necessary mainly for the reason of having an objective and automatic way of quantifying creative artifacts. In this work, we propose a regression-based learning framework which takes into account quantitatively the essential criteria for creativity like novelty, influence, value and unexpectedness. As it is often the case with most creative domains, there is no clear ground truth available for creativity. Our proposed learning framework is applicable to all creative domains; yet we evaluate it on a dataset of movies created from IMDb and Rotten Tomatoes due to availability of audience and critic scores, which can be used as proxy ground truth labels for creativity. We report promising results and observations from our experiments in the following ways : 1) Correlation of creative criteria with critic scores, 2) Improvement in movie rating prediction with inclusion of various creative criteria, and 3) Identification of creative movies.


Partner Personas Generation for Diverse Dialogue Generation

Incorporating personas information allows diverse and engaging responses...

A metric for evaluating 3D reconstruction and mapping performance with no ground truthing

It is not easy when evaluating 3D mapping performance because existing m...

Estimating regression errors without ground truth values

Regression analysis is a standard supervised machine learning method use...

New Performance Measures for Object Tracking under Complex Environments

Various performance measures based on the ground truth and without groun...

On Assessing the Usefulness of Proxy Domains for Developing and Evaluating Embodied Agents

In many situations it is either impossible or impractical to develop and...

Leveraging Implicit Expert Knowledge for Non-Circular Machine Learning in Sepsis Prediction

Sepsis is the leading cause of death in non-coronary intensive care unit...

Criteria Comparative Learning for Real-scene Image Super-Resolution

Real-scene image super-resolution aims to restore real-world low-resolut...