Neither hype nor gloom do DNNs justice

Area of Science:

• Computer Vision
• Computational Neuroscience
• Artificial Intelligence

Background:

• Deep neural networks (DNNs) are increasingly utilized as models in vision science.
• There is ongoing debate regarding the capabilities and limitations of DNNs in explaining visual processing.
• Exaggerated claims and overly critical views may misrepresent the potential of DNNs.

Purpose of the Study:

• To provide a balanced perspective on the utility of DNNs as models in vision science.
• To highlight the rapid evolution and potential of DNNs.
• To emphasize the importance of multiple model desiderata, including explanation and prediction.

Main Methods:

• Review of current literature on DNNs in vision science.
• Analysis of the evolving capabilities of DNNs.
• Discussion of model evaluation criteria.

Main Results:

• DNNs are rapidly advancing, suggesting that current limitations may be transient.
• Both explanatory power and predictive accuracy are essential for robust models in vision science.
• A balanced approach considering multiple model desiderata is necessary.

Conclusions:

• DNNs hold significant promise as models for understanding vision science.
• Future research should focus on developing DNNs that offer both strong predictive performance and interpretable explanations.
• A nuanced view, acknowledging both progress and challenges, is crucial for the field.