Image for representational purpose (Pic: PTI)
Researchers from the Indian Institute of Science (IISc) in their study have found crucial qualitative differences between the human brain and Deep Neural Networks, and these gaps can be filled by training the deep networks on larger datasets, incorporating more constraints or by modifying network architecture.
The team from the Centre for Neuroscience (CNS) studied 13 different perceptual effects and found that Convolutional or deep neural networks that have their object representations match coarsely with the brain are still outperformed by humans. Lots of studies have been showing similarities between deep networks and brains, but no one has really looked at systematic differences, said SP Arun, Associate Professor at CNS and senior author of the study in a note by the institute. Identifying these differences can push us closer to making these networks more brain-like, he added.