Domain-Adversarial Neural Networks DANN
Tags: #machine learning #transfer learningEquation
$$\min [\frac{1}{m}\sum^{m}_{1}\mathcal{L}(f(\textbf{x}^{s}_{i}),y_{i})+\lambda \max(-\frac{1}{m}\sum^{m}_{i=1}\mathcal{L}^{d}(o(\textbf{x}^{s}_{i}),1)-\frac{1}{m^{'}}\sum^{m^{'}}_{i=1}\mathcal{L}^{d}(o(\textbf{x}^{t}_{i}),0))]$$Latex Code
\min [\frac{1}{m}\sum^{m}_{1}\mathcal{L}(f(\textbf{x}^{s}_{i}),y_{i})+\lambda \max(-\frac{1}{m}\sum^{m}_{i=1}\mathcal{L}^{d}(o(\textbf{x}^{s}_{i}),1)-\frac{1}{m^{'}}\sum^{m^{'}}_{i=1}\mathcal{L}^{d}(o(\textbf{x}^{t}_{i}),0))]
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Introduction
Equation
Latex Code
\min [\frac{1}{m}\sum^{m}_{1}\mathcal{L}(f(\textbf{x}^{s}_{i}),y_{i})+\lambda \max(-\frac{1}{m}\sum^{m}_{i=1}\mathcal{L}^{d}(o(\textbf{x}^{s}_{i}),1)-\frac{1}{m^{'}}\sum^{m^{'}}_{i=1}\mathcal{L}^{d}(o(\textbf{x}^{t}_{i}),0))]
Explanation
In this formulation of Domain-Adversarial Neural Networks(DANN), authors add a domain adaptation regularizer term to the original loss function of source domain. The domain adaptation regularizer term are calculated based on the H-divergence of two distributions h(X_{S}) and h(X_{T}). The adversial network aims to maximize the likelihood that the domain classifier are unable to distingush a data point belongs to source domain S or target domain T. Function o(.) is the domain regressor which learns high level representation o(X) given input X. You can check more detailed information in this paper by Hana Ajakan, Pascal Germain, et al., Domain-Adversarial Neural Networks for more details.
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