Spherical Autoencoder for Object Discovery Beyond Slot Limits
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Solution Overview
Problem
Current machine learning systems for object discovery are limited by complex architectures and intricate training schemes, particularly in slot-based approaches, which restrict the number of objects they can represent effectively.
Innovation Solution
A computer-implemented method using a spherical autoencoder that encodes and decodes input data in spherical coordinates to generate latent representation data, including radial and phase components, allowing for efficient object-centric representations and reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If slot-based approaches are used for object discovery, then object features can be separated into slots, but the architecture becomes complex and the number of representable objects is limited
Solution Approach 1:
The patent transforms the representation space from Cartesian coordinates to spherical coordinates, changing the parameterization of latent representations. This parameter change allows the model to represent a greater number of objects without increasing architectural complexity, as the spherical coordinate system naturally handles variable numbers of objects through radial and angular components
Solution Approach 2:
The patent explicitly adopts spherical coordinates for the latent representation space, using a radial component and angular components to encode object information. This spherical structure replaces the traditional slot-based Cartesian approach, enabling the model to represent any number of objects through the angular dimensions while keeping the architecture simple and unified
2Measurement precision
If slot-based approaches are used for object discovery, then object features can be separated, but intricate training schemes are required
Solution Approach 1:
By changing to spherical coordinates with radial and angular components, the training process becomes more straightforward. The radial component naturally captures object presence and the angular components capture object identity and position, eliminating the need for complex iterative training schemes required by slot-based methods
Solution Approach 2:
The spherical coordinate system inherently provides the separation of object features through its mathematical structure. The radial and angular components automatically disentangle object properties during training without requiring external slot assignment mechanisms or complex training procedures, making the system self-organizing
3Quantity of substance
If complex autoencoder with complex valued activations is used, then object-centric representations can be learned, but the number of representable objects is restricted
Solution Approach 1:
The patent uses spherical coordinates where the radial distance and angular positions can represent any number of objects. The spherical structure provides natural capacity scaling through its geometric properties, allowing the model to represent an unbounded number of objects without increasing model complexity or requiring complex-valued activations
Data Source
AI summary
A computer-implemented system and method relate to object discovery. The system and method include receiving a source image and generating input data by associating each pixel of the source image with predetermined phase values. An encoder encodes the input data to generate latent representation data in spherical coordinates. A decoder decodes the latent representation data to generate spherical reconstruction data of the source image. The spherical reconstruction data includes a radial component and a plurality of phase components. A reconstructed image is generated based at least on the radial component. The reconstructed image is a reconstruction of the source image.