Convolutional Neural Networks Can (Meta-)Learn the Same-Different Relation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914026854809600 |
|---|---|
| author | Gupta, Max Rane, Sunayana McCoy, R. Thomas Griffiths, Thomas L. |
| author_facet | Gupta, Max Rane, Sunayana McCoy, R. Thomas Griffiths, Thomas L. |
| contents | While convolutional neural networks (CNNs) have come to match and exceed human performance in many settings, the tasks these models optimize for are largely constrained to the level of individual objects, such as classification and captioning. Humans remain vastly superior to CNNs in visual tasks involving relations, including the ability to identify two objects as `same' or `different'. A number of studies have shown that while CNNs can be coaxed into learning the same-different relation in some settings, they tend to generalize poorly to other instances of this relation. In this work we show that the same CNN architectures that fail to generalize the same-different relation with conventional training are able to succeed when trained via meta-learning, which explicitly encourages abstraction and generalization across tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23212 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Convolutional Neural Networks Can (Meta-)Learn the Same-Different Relation Gupta, Max Rane, Sunayana McCoy, R. Thomas Griffiths, Thomas L. Computer Vision and Pattern Recognition Machine Learning 68T07 I.2.0; I.2.6 While convolutional neural networks (CNNs) have come to match and exceed human performance in many settings, the tasks these models optimize for are largely constrained to the level of individual objects, such as classification and captioning. Humans remain vastly superior to CNNs in visual tasks involving relations, including the ability to identify two objects as `same' or `different'. A number of studies have shown that while CNNs can be coaxed into learning the same-different relation in some settings, they tend to generalize poorly to other instances of this relation. In this work we show that the same CNN architectures that fail to generalize the same-different relation with conventional training are able to succeed when trained via meta-learning, which explicitly encourages abstraction and generalization across tasks. |
| title | Convolutional Neural Networks Can (Meta-)Learn the Same-Different Relation |
| topic | Computer Vision and Pattern Recognition Machine Learning 68T07 I.2.0; I.2.6 |
| url | https://arxiv.org/abs/2503.23212 |