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Found 7 papers, 1 papers with code
BRACTIVE: A Brain Activation Approach to Human Visual Brain Learning
Our experiments demonstrate that BRACTIVE effectively identifies person-specific regions of interest, such as face and body-selective areas, aligning with neuroscience findings and indicating potential applicability to various object categories.
Brainformer: Mimic Human Visual Brain Functions to Machine Vision Models via fMRI
This loss function mimics brain activity patterns from these regions in the deep neural network using fMRI data.
Information Transfer Rate in BCIs: Towards Tightly Integrated Symbiosis
In order to calculate ITR, it is customary to assume a uniform input distribution and an oversimplified channel model that is memoryless, stationary, and symmetrical in nature with discrete alphabet sizes.
Neural Correlates of Face Familiarity Perception
In the domain of face recognition, there exists a puzzling timing discrepancy between results from macaque neurophysiology on the one hand and human electrophysiology on the other.
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations
While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations and illuminations, Deep Neural Networks (DNNs) severely suffer in this case, even when large amounts of training examples are available.
Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations
The capability of Deep Neural Networks (DNNs) to recognize objects in orientations outside the distribution of the training data is not well understood.
Robustness to Transformations Across Categories: Is Robustness To Transformations Driven by Invariant Neural Representations?
Our results with state-of-the-art DCNNs indicate that invariant neural representations do not always drive robustness to transformations, as networks show robustness for categories seen transformed during training even in the absence of invariant neural representations.
