Neural Network Representation Learning via Mutual Information Lower Bound
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Solution Overview
Problem
Existing deep representation learning methods for tasks like image retrieval and cross-modal learning are inefficient due to sensitivity to sampling strategies and require direct computation of similarity distributions, which can be challenging and inefficient.
Innovation Solution
A ranking-based loss function that uses information theory to estimate the overlap between similarity distributions of positive and negative pairs without direct computation, incorporating a gradient penalty method to enforce correct ordering and applicable to various similarity functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If triplet loss or quadruplet loss is used to compute and aggregate differences between similarities of positive and negative pairs, then representation learning can be performed, but the method becomes sensitive to sampling strategy and suffers from inefficiency in sampling
Solution Approach 1:
The patent replaces the mechanical sampling process with a neural network-based approach. Instead of manually selecting positive and negative pairs through sampling strategies, a neural network automatically learns to generate hard positive and negative pairs by maximizing a lower bound on mutual information. This substitution eliminates sampling sensitivity and improves efficiency by leveraging the neural network's ability to automatically identify informative pairs during training.
2Productivity
If histogram loss is used to provide empirical estimates of distributions and calculate probability of negative pairs having higher similarity, then sampling efficiency improves, but the method still requires direct computation of similarity distributions which is challenging and inefficient
Solution Approach 1:
The patent introduces a neural network as an intermediary that computes a lower bound on mutual information between positive and negative pairs. This intermediary function serves as a proxy for directly computing similarity distributions, avoiding the need to explicitly model and compute complex probability distributions. The neural network learns to approximate the distribution characteristics through the mutual information lower bound, simplifying the computational burden while maintaining effectiveness.
Solution Approach 2:
The patent transforms the problem from computing similarity distributions directly to optimizing a lower bound on mutual information. By changing the parameter being optimized from distribution-based metrics to information-theoretic bounds, the method avoids the computational complexity of direct distribution estimation while preserving the ability to learn meaningful representations through gradient-based optimization.
3Measurement precision
If complex neural network architectures are used to transform input into embedding space, then high proximity for semantically related items can be achieved, but the system complexity increases
Solution Approach 1:
The patent employs self-service mechanisms where the neural network automatically generates hard positive and negative pairs during training without external intervention. The system serves itself by using its own outputs to generate training examples, eliminating the need for complex manual curation or sophisticated sampling strategies. This self-service approach simplifies the overall system architecture while maintaining high representation quality through automatic hard example mining.
Data Source
AI summary
An apparatus for providing similar contents, using a neural network, includes a memory storing instructions, and a processor configured to execute the instructions to obtain a plurality of similarity values between a user query and a plurality of images, using a similarity neural network, obtain a rank of each the obtained plurality of similarity values, and provide, as a most similar image to the user query, at least one among the plurality of images that has a respective one among the plurality of similarity values that corresponds to a highest rank among the obtained rank of each of the plurality of similarity values. The similarity neural network is trained with a divergence neural network for outputting a divergence between a first distribution of first similarity values for positive pairs, among the plurality of similarity values, and a second distribution of second similarity values for negative pairs, among the plurality of similarity values.