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Found 5 papers, 2 papers with code
MindSet: Vision. A toolbox for testing DNNs on key psychological experiments
Multiple benchmarks have been developed to assess the alignment between deep neural networks (DNNs) and human vision.
Lost in Latent Space: Disentangled Models and the Challenge of Combinatorial Generalisation
Recent research has shown that generative models with highly disentangled representations fail to generalise to unseen combination of generative factor values.
Auto-Tag: Tagging-Data-By-Example in Data Lakes
As data lakes become increasingly popular in large enterprises today, there is a growing need to tag or classify data assets (e. g., files and databases) in data lakes with additional metadata (e. g., semantic column-types), as the inferred metadata can enable a range of downstream applications like data governance (e. g., GDPR compliance), and dataset search.
The role of Disentanglement in Generalisation
It is claimed that such representations should be able to capture the compositional structure of the world which can then be combined to produce novel representations.
What a difference a pixel makes: An empirical examination of features used by CNNs for categorisation
Convolutional neural networks (CNNs) were inspired by human vision and, in some settings, achieve a performance comparable to human object recognition.
