Deep Neural Network Visual

This approach integrates multiple artificial deep neural networks trained on a diverse set of functions with functional recordings of the whole human brain. Our results reveal a systematic tiling of visual cortex by mapping regions to particular functions of the deep networks.

Keywords deep convolutional neural networks, generalization, drawings, representational similarity analysis Introduction Humans have the remarkable ability to robustly recognize objects across a wide range of visual abstractions.

Recent research has delved into the biological parallels between deep neural networks DNNs in vision and human perception through the study of visual illusions. However, the bulk of this research is currently constrained to the investigation of visual illusions within a single model focusing on a singular type of illusion. There exists a need for a more comprehensive explanation of visual

The past decade has witnessed the superior power of deep neural networks DNNs in applications across various domains. However, training a high-quality DNN remains a non-trivial task due to its massive number of parameters. Visualization has shown great potential in addressing this situation, as evidenced by numerous recent visualization works that aid in DNN training and interpretation

Here, the authors show that a multi-branch deep neural network can predict neural activity independently in visual areas in the absence of hierarchical representations.

SIGNIFICANCE STATEMENT Visual perceptual learning VPL has been found to cause changes at multiple stages of the visual hierarchy. We found that training a deep neural network DNN on an orientation discrimination task produced behavioral and physiological patterns similar to those found in human and monkey experiments.

Deep neural networks that are trained either on images or on text or by pairing images and text enable us now to disentangle human mental representations into their visual, visual-semantic and

Understanding visual perceptual learning VPL has become increasingly more challenging as new phenomena are discovered with novel stimuli and training paradigms. Although existing models aid our knowledge of critical aspects of VPL, the connections shown by these models between behavioral learning and plasticity across different brain areas are typically superficial. Most models explain VPL

TensorSpace TensorSpace is a neural network 3D visualization framework built by TensorFlow.js, Three.js and Tween.js. TensorSpace provides Layer APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser.

We re-investigate the pipeline of fine-grained visual categorization FGVC techniques from the view of human visual recognition system, and propose a novel Attention-Shift based Deep Neural Network AS-DNN for automatic parts locating and semantic correlation learning.