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Noise increases the correspondence between artificial and human vision.

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Deep neural networks (DNNs) trained with noisy data better replicate human visual processing. This advancement improves artificial intelligence

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Area of Science:

  • Computer Vision
  • Neuroscience
  • Artificial Intelligence

Background:

  • Deep neural networks (DNNs) are foundational to state-of-the-art computer vision.
  • Understanding how DNNs process visual information is crucial for advancing AI and neuroscience.
  • Current DNNs often do not fully capture the nuances of human visual perception.

Purpose of the Study:

  • To investigate if training DNNs on noisy stimuli enhances their ability to model human visual responses.
  • To compare the performance of DNNs trained on noisy versus standard stimuli in mirroring human behavior and neural activity.

Main Methods:

  • Utilized deep neural networks (DNNs) for visual processing tasks.
  • Trained DNNs using both standard and noisy visual stimuli.
  • Evaluated DNN performance against human behavioral data and neural recordings.

Main Results:

  • DNNs trained on noisy stimuli demonstrated superior performance compared to standard DNNs.
  • The noisy-trained DNNs more accurately mirrored human behavioral responses to visual stimuli.
  • Neural responses in DNNs trained on noisy data showed closer alignment with human neural visual responses.

Conclusions:

  • Training deep neural networks on noisy stimuli is an effective strategy to improve their biological plausibility.
  • This approach offers a promising avenue for developing more human-like artificial vision systems.
  • Findings suggest noisy stimuli training could bridge the gap between artificial and biological visual processing.