Development of Few-Shot Learning Capabilities in Artificial Neural Networks When Learning Through Self-Supervised...

Area of Science:

• Artificial Intelligence
• Cognitive Science
• Neuroscience

Background:

• Traditional object recognition relies on resource-intensive supervised learning with large labeled datasets.
• Current methods differ significantly from natural human learning processes.
• Supervised learning poses challenges in data acquisition and scalability.

Purpose of the Study:

• To develop a more efficient and human-like learning paradigm for artificial agents.
• To enable rapid semantic concept association using self-supervised learning.
• To investigate the effectiveness of curiosity-driven exploration in representation learning.

Main Methods:

• An artificial agent learns in a simulated environment through self-supervised, curiosity-driven exploration.
• Learned representations are utilized for fast concept mapping, identifying semantic concepts via correlated neural firing patterns.
• The fast concept mapping method allows instantaneous association with few labeled examples.

Main Results:

• Object identification achieved with as few as one labeled example, demonstrating high-quality self-supervised encoding.
• The proposed method significantly outperforms non-interactive approaches in few-shot learning scenarios.
• Meaningful environmental representations are learned through pure interaction.

Conclusions:

• Self-supervised learning through interaction offers a feasible strategy for concept acquisition with reduced supervision.
• The fast concept mapping technique effectively leverages learned representations for rapid semantic association.
• This approach presents a promising direction for developing more adaptable and efficient AI systems.