Image Representation Training with Hard Negative Similarity Loss
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Solution Overview
Problem
Existing unsupervised contrastive learning methods struggle to effectively train image representation models using unlabeled data, as they fail to adequately distinguish between positive and negative samples, leading to suboptimal performance in image classification tasks.
Innovation Solution
A method involving generating anchor image embedding vectors, determining similarities between anchor images and positive/negative samples, and calculating a loss value based on these similarities, with weights applied to enhance the training process, using dictionaries to store and retrieve label similarities for improved model updating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing unsupervised contrastive learning methods are used to train image representation models, then training can be performed using unlabeled data, but the model fails to adequately distinguish between positive and negative samples resulting in suboptimal classification performance
Solution Approach 1:
The patent applies local quality by differentiating the treatment of positive and negative samples through separate similarity determination processes. First similarities are calculated for negative samples while second similarities are calculated for positive samples, allowing the model to apply different discrimination standards to different sample types, thereby improving both sample distinction accuracy and classification performance
Solution Approach 2:
The patent changes parameters by introducing multiple similarity metrics (first similarities and second similarities) and using them differently in the loss calculation. The loss value incorporates both the first similarities for negative samples and second similarities for positive samples, allowing dynamic adjustment of discrimination thresholds based on sample type, which resolves the contradiction between reliable classification and precise sample distinction
2Productivity
If standard contrastive loss is used without label information, then unlabeled data can be utilized, but the model cannot leverage label similarities to improve training effectiveness
Solution Approach 1:
The patent applies preliminary action by pre-computing label similarities between the anchor image and all other samples before performing the contrastive learning optimization. This pre-computed label similarity information is then integrated into the loss function, allowing the model to leverage label relationships in advance, thereby improving training effectiveness without losing label information
Solution Approach 2:
The patent merges unsupervised contrastive learning with supervised label information by combining first similarities (from unlabeled data), second similarities (from labeled data), and label similarities into a unified loss function. This merging allows the model to simultaneously utilize both unlabeled data efficiency and label information guidance, resolving the contradiction between productivity and information loss
Data Source
AI summary
A method generates an anchor image embedding vector for an anchor image using an image representation model, determine first similarities between the anchor image and negative samples of the anchor image using first image embedding vectors for the negative samples and the generated anchor image embedding vector, determine second similarities between the anchor image and positive samples of the anchor image using second image embedding vectors for the positive samples and the generated anchor image embedding vector, obtain one of a vector corresponding to a label of the anchor image and third similarities between the label of the anchor image and labels of the negative samples, determine a loss value for the anchor image based on the determined first similarities, and the determined second similarities, and one of the obtained third similarities and a fourth similarity.