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Found 6 papers, 3 papers with code
Distinguishing representational geometries with controversial stimuli: Bayesian experimental design and its application to face dissimilarity judgments
Comparing representations of complex stimuli in neural network layers to human brain representations or behavioral judgments can guide model development.
Unsupervised learning of features and object boundaries from local prediction
A visual system has to learn both which features to extract from images and how to group locations into (proto-)objects.
Statistical inference on representational geometries
However, we lack robust methods for connecting theory and experiment by evaluating our new big models with our new big data.
Deep Neural Models for color discrimination and color constancy
High levels of color constancy were achieved with different DNN architectures.
Generalisation in humans and deep neural networks
We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations.
Comparing deep neural networks against humans: object recognition when the signal gets weaker
In addition, we find progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker, indicating that there may still be marked differences in the way humans and current DNNs perform visual object recognition.
