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Found 7 papers, 4 papers with code
An adaptive homeostatic algorithm for the unsupervised learning of visual features
The formation of structure in the brain, that is, of the connections between cells within neural populations, is by large an unsupervised learning process: the emergence of this architecture is mostly self-organized.
Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?
An important milestone for AI is the development of algorithms that can produce drawings that are indistinguishable from those of humans.
Diversity vs. Recognizability: Human-like generalization in one-shot generative models
Robust generalization to new concepts has long remained a distinctive feature of human intelligence.
Iterative VAE as a predictive brain model for out-of-distribution generalization
Our ability to generalize beyond training data to novel, out-of-distribution, image degradations is a hallmark of primate vision.
Effect of top-down connections in Hierarchical Sparse Coding
In this study, a new model called 2-Layers Sparse Predictive Coding (2L-SPC) is introduced to assess the impact of this inter-layer feedback connection.
Sparse Deep Predictive Coding captures contour integration capabilities of the early visual system
Third, we demonstrate at the representational level that the SDPC feedback connections are able to overcome noise in input images.
From biological vision to unsupervised hierarchical sparse coding
The formation of connections between neural cells is emerging essentially from an unsupervised learning process.
