The authors propose a system for visual scene analysis and recognition that combines convolutional sparse coding and resonator networks. Convolutional sparse coding is used to learn a sparse, latent feature representation of an image, which is then encoded into a high-dimensional vector and factorized by a resonator network.
The key insights are:
Convolutional sparse coding provides an equivariant, data-adaptive encoding scheme that reduces redundancy in the image representation, making it more suitable for factorization by the resonator network.
The integration of sparse coding with the resonator network increases the capacity of distributed representations and reduces collisions in the combinatorial search space during factorization.
The resonator network is capable of fast and accurate vector factorization, and the authors develop a confidence-based metric that assists in tracking the convergence of the resonator network.
The authors demonstrate the benefits of their approach on multiple datasets, including "Random Bars", "Translated MNIST", and "Letters". They show that the sparse representations consistently outperform pixel-based encodings in terms of accuracy, convergence speed, and the ability to handle scenes with multiple objects.
Additionally, the authors discuss the connections of their work to existing models in computational neuroscience and vector symbolic architectures, as well as potential future directions, such as extending the approach to handle more complex transformations and implementing it on neuromorphic hardware.
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by Christopher ... at arxiv.org 05-01-2024
https://arxiv.org/pdf/2404.19126.pdfDeeper Inquiries