Food Taste Similarity Prediction Based on Images and Human Judgments

Food Taste Similarity Triplets


A dataset of images of 10,000 dishes, together with a set of triplets representing human annotations of food taste similarity is provided. The objective of the project is to predict for unseen triplets $(A, B, C)$, whether dish $A$ is more similar in taste to $B$ or $C$. In this project, image embeddings are learned through an autoencoder, where the loss is computed based on distances between the embeddings.

Chenhao Li
Chenhao Li
Reinforcement Learning for Robotics

My research interests focus on the general field of robot learning, including reinforcement learning, developmental robotics and legged intelligence.