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About Domain-Adversarial Training of Neural Networks
This paper introduces a new domain adaptation approach for neural networks called domain-adversarial training. The approach aims to learn representations that are discriminative for the main learning task but also invariant to the shift between the source and target domains. This is achieved by jointly training a label predictor and a domain classifier in an adversarial manner. The approach can be implemented by augmenting any feed-forward neural network with additional layers and losses, allowing it to be trained end-to-end using standard methods like backpropagation.
Detailed Information
| Author: | ['Ripper Reaper'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 35 |
| Language: | English |
| Format: | |
| Price: | FREE |
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