4 citations · 13 across the 20 of their papers we have counts for
3 papers · 1 filter
It's Not a Modality Gap: Characterizing and Addressing the Contrastive Gap
Abrar Fahim, Alex Murphy, Alona Fyshe
Multi-modal contrastive models such as CLIP achieve state-of-the-art performance in zero-shot classification by embedding input images and texts on a joint representational space.…
Improving the Accuracy and Robustness of CNNs Using a Deep CCA Neural Data Regularizer
Cassidy Pirlot, Richard C. Gerum, Cory Efird +2
As convolutional neural networks (CNNs) become more accurate at object recognition, their representations become more similar to the primate visual system. This finding has inspire…
Improved object recognition using neural networks trained to mimic the brain's statistical properties
Callie Federer, Haoyan Xu, Alona Fyshe +1
The current state-of-the-art object recognition algorithms, deep convolutional neural networks (DCNNs), are inspired by the architecture of the mammalian visual system, and are cap…