Segmented Embedding Coding to Prevent SSRL Representation Collapse
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Solution Overview
Problem
Existing self-supervised representation learning methods often produce trivial solutions where all samples have the same embedding, lacking discriminative and transform-invariant features, and require high computational and memory costs.
Innovation Solution
The proposed MUlti-Segmental Informational Coding (MUSIC) technique partitions embedding feature vectors into segments corresponding to different attributes, using entropy-based loss functions to maximize joint entropy and transformation invariance, avoiding trivial solutions and reducing computational and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If similarity maximization is used to enforce semantically similar samples close to each other in embedding space, then semantic consistency is improved, but the solution becomes trivial with all samples having the same embedding
Solution Approach 1:
The patent segments the embedding space into multiple attribute-specific subspaces, each governed by its own entropy regularization term. This segmentation prevents collapse to a single embedding by ensuring diverse representations within each attribute dimension while maintaining semantic consistency across transformations.
Solution Approach 2:
The patent introduces entropy regularization as a parameter-based constraint that dynamically controls the distribution of embeddings. By maximizing entropy within attribute subspaces, the system maintains parameter diversity (embedding variation) while preserving the semantic consistency through the contrastive learning objective.
2Reliability
If large batch sizes and deep projection heads are used to avoid trivial solutions, then embedding quality is improved, but computational and memory costs increase
Solution Approach 1:
By segmenting the embedding space into attribute-specific subspaces, the patent enables more efficient computation. Each attribute subspace can be optimized independently with smaller computational resources, avoiding the need for uniformly large batch sizes and deep projection heads across the entire embedding space.
Solution Approach 2:
The entropy regularization parameter provides a computationally efficient mechanism to control embedding quality without requiring increases in batch size or projection head depth. This parameter-based approach achieves better embedding quality at lower computational cost by directly influencing the distribution characteristics rather than relying on architectural scaling.
3Reliability
If all samples are mapped to the same embedding to maximize similarity, then semantic consistency is improved, but discriminative and transform-invariant features are lost
Solution Approach 1:
The patent segments embeddings into attribute-specific components, allowing each segment to maintain discriminative power for its corresponding attribute while collectively preserving semantic consistency. This segmentation enables the model to differentiate between samples based on attribute variations without compromising overall semantic alignment.
Solution Approach 2:
By introducing entropy maximization as a parameter-based constraint, the patent ensures that embeddings maintain sufficient variation and discriminative power. The entropy parameter controls the balance between consistency and discrimination, preventing collapse to identical embeddings while preserving transform-invariant features through the regularization mechanism.
Data Source
AI summary
In one embodiment, there is provided a self-supervised representation learning (SSRL) circuitry. The SSRL circuitry includes a transformer circuitry configured to receive input data. The input data includes an input batch containing a number, N, of input data sets. The transformer circuitry is configured to transform the input batch into a plurality of training batches. Each training batch contains the number N training data sets. The SSRL circuitry further includes for each training batch: a respective encoder circuitry, a respective projector circuitry, and a respective partitioning circuitry. The respective encoder circuitry is configured to encode each training data set into a respective representation feature. The respective projector circuitry is configured to map each representation feature into an embedding space as a respective embedding feature vector.