Conditional Generator Network for Hard Negative Sample Synthesis
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Solution Overview
Problem
Current neural network training methods for Generative Adversarial Networks (GANs) face challenges in discriminativeness and sharpness due to the lack of effective negative examples, particularly in supervised and unsupervised learning settings, where easy negative examples do not force the model to identify critical characteristics.
Innovation Solution
The implementation of a conditional generator network within a GAN framework that generates hard negative samples based on an adaptive distribution, using a combination of fixed noise and learnable parameters to improve the discriminative capabilities of the model, particularly in word embedding and cross-modal retrieval tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If easy negative examples are used in training, then training efficiency is improved, but discriminativeness and sharpness of the model deteriorate
Solution Approach 1:
The patent applies dynamics by transitioning from static easy negative examples to dynamic hard negative examples that are adaptively generated during training. The generator network evolves to produce increasingly difficult negative samples that challenge the discriminator, forcing it to learn more discriminative features while maintaining training progress
Solution Approach 2:
The patent changes the parameter of negative example difficulty from easy to hard through the use of a generator network with learnable parameters. The generator's parameters are optimized to produce negative examples that maximize the challenge to the discriminator, thereby improving discriminativeness without sacrificing training efficiency
2Measurement precision
If hard negative samples are generated using adaptive distribution, then model discriminativeness is improved, but training complexity increases
Solution Approach 1:
The patent segments the training process into two distinct networks: a generator responsible for producing hard negative samples and a discriminator responsible for learning discriminative features. This segmentation allows each component to specialize and improves overall system performance despite the increased complexity
Solution Approach 2:
The generator network acts as an intermediary that bridges the gap between simple data sampling and complex discriminative learning. By introducing this intermediate component that generates adaptive hard negatives, the system achieves better discriminativeness while managing complexity through modular architecture
Data Source
AI summary
An electronic device for neural network training includes at least one processor and one or more memories configured to provide or train: a generative adversarial network (GAN) using a generator and a discriminator for: receiving a plurality of training cases; and training the generative adversarial network, based on the plurality of training cases, to classify the training cases; wherein the generator generates hard negative examples for the discriminator.