Abstract
Raven's Progressive Matrices are a benchmark originally designed to test the cognitive abilities of humans. It has recently been adapted to test relational reasoning in machine learning systems. For this purpose the so-called Procedurally Generated Matrices dataset was set up, which is so far one of the most difficult relational reasoning benchmarks. Here we show that deep neural networks are capable of solving this benchmark, reaching an accuracy of 98.0 percent over the previous state-of-the-art of 62.6 percent by combining Wild Relation Networks with Multi-Layer Relation Networks and introducing Magnitude Encoding, an encoding scheme designed for late fusion architectures.
| Original language | English |
|---|---|
| Title of host publication | 2020 International Joint Conference on Neural Networks (IJCNN) |
| Publisher | IEEE |
| Publication date | 07.2020 |
| Article number | 9207319 |
| ISBN (Print) | 978-1-7281-6927-9 |
| ISBN (Electronic) | 978-1-7281-6926-2 |
| DOIs | |
| Publication status | Published - 07.2020 |
| Event | 2020 International Joint Conference on Neural Networks - Virtual, Glasgow, United Kingdom Duration: 19.07.2020 → 24.07.2020 Conference number: 163566 |
Research Areas and Centers
- Research Area: Intelligent Systems
- Centers: Center for Artificial Intelligence Luebeck (ZKIL)
DFG Research Classification Scheme
- 4.43-05 Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
