An afternoon walk turns into a few hours basking in this scene. Custom deep dream implementation (GoogLeNet model trained on ImageNet) augmented with optical flow fields.
This collection is an exploration of AI-based vision systems with the goal of giving the viewer a sense of pareidolia. The code used to generate these dreams was created through my research into visual perception. By exploring these networks, I hope to gain an understanding of our own visual perception systems. Each piece is unique 1/1. There will only ever be 36 unique pieces created.
Deep dream is one of the earliest techniques that was used to interpret what deep convolutional neural networks trained for computer vision tasks "dream" of. Additionally, there has been ample recent research suggesting these models do learn to "perceive" the world similarly to our own visual system. This collection is largely based on such research.
In the Garden at Noon
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In the Garden at Noon
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An afternoon walk turns into a few hours basking in this scene. Custom deep dream implementation (GoogLeNet model trained on ImageNet) augmented with optical flow fields.
This collection is an exploration of AI-based vision systems with the goal of giving the viewer a sense of pareidolia. The code used to generate these dreams was created through my research into visual perception. By exploring these networks, I hope to gain an understanding of our own visual perception systems. Each piece is unique 1/1. There will only ever be 36 unique pieces created.
Deep dream is one of the earliest techniques that was used to interpret what deep convolutional neural networks trained for computer vision tasks "dream" of. Additionally, there has been ample recent research suggesting these models do learn to "perceive" the world similarly to our own visual system. This collection is largely based on such research.