Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models

Keyphrases

• Computational Model 100%
• Visual Representation 100%
• Representational Similarity Analysis 100%
• Mixed Features 100%
• Feature Space 60%
• Training Set 40%
• Linear Transformation 40%
• Model Features 40%
• Ventral Stream 40%
• Representational Space 40%
• Gabor Wavelet 40%
• Wavelet Pyramid 40%
• Task Performance 20%
• Functional Magnetic Resonance Imaging 20%
• Neural Network 20%
• Brain Activity 20%
• Performance Optimization 20%
• Cortical Areas 20%
• Realistic Model 20%
• Response Pattern 20%
• Lateral Occipital Complex 20%
• Natural Images 20%
• Semantic Dimension 20%
• Object Classification 20%
• Nonlinear Model 20%
• Backpropagation 20%
• Primate Visual System 20%
• Response Profiles 20%
• Activity Data 20%
• Occipital Region 20%
• Model Response 20%
• Major Constraints 20%
• Model Representation 20%
• Pyramid Model 20%
• Natural Experience 20%
• Brain Representation 20%
• Convolutional Networks 20%
• Computational Objects 20%
• Shallow Model 20%
• Population Receptive Field Modeling 20%
• Hypothesis Space 20%
• Deep Convolutional Network 20%
• Object Vision 20%
• Early Visual Areas 20%
• Many Parameters 20%
• Vision Model 20%
• Higher Layers 20%

Computer Science

• Computational Modeling 100%
• Feature Space 100%
• Visual Representation 100%
• Linear Transformation 66%
• Channel Response 66%
• Ventral Stream 66%
• Early Visual Area 33%
• Task Performance 33%
• Performance Optimization 33%
• Convolutional Neural Network 33%
• Representation Model 33%
• Convolutional Network 33%
• Neural Network 33%
• Brain Activity 33%
• Response Pattern 33%
• Fitting Parameter 33%
• Hypothesis Space 33%
• Receptive Field 33%
• Nonlinear Model 33%
• Electroencephalogram 33%