Domain Adaptation
Daniel Yukimura
Nov 7th, 2019
through adversarial training
Domain Adaptation
Has deep learning solved all our vision problems?
Domain Adaptation
Domain Adaptation
Different Domains | Same Task
Domain Adaptation
(\mathcal{X}_s, P_s)
Source data
Target data
(\mathcal{X}_t, P_t)
D_s = \{ (x_i, y_i)\}_{i = 1}^{N_s}
D_t = \{ (\tilde{x}_j, \cdot)\}_{j = 1}^{N_t}
Domain Adaptation (DA)
Scenarios:
- Homogeneous DA: Heterogeneous DA:
- Supervised, Semi-Supervised, or Unsupervised
- One-step or Multi-step DA
\mathcal{X}_s = \mathcal{X}_t
\mathcal{X}_s \neq \mathcal{X}_t
D_t = \{ (\tilde{x}_j, \cdot)\}_{j = 1}^{N_t}
\left(P_s \neq P_t\right)
Domain Adaptation (DA)
Scenarios:
- Homogeneous DA: Heterogeneous DA:
- Supervised, Semi-Supervised, or Unsupervised
- One-step or Multi-step DA
\mathcal{X}_s = \mathcal{X}_t
\mathcal{X}_s \neq \mathcal{X}_t
D_t = \{ (\tilde{x}_j, \cdot)\}_{j = 1}^{N_t}
\left(P_s \neq P_t\right)
Today's approach: Adversarial-based Domain Adaptation
A Review on GANs & Adversarial Training
A Review on GANs and Adversarial Training
Generator Functions:
A generator can map a known distribution to the distribution on the feature space:
X \overset{d}{=} g(Z)
g:\mathcal{Z} \rightarrow \mathcal{X}
A Review on GANs and Adversarial Training
Adversarial Training:
Design a game between machines where the equilibrium solves a learning problem.
GANs:
- Generator:
- Discriminator:
- Game:
G: \mathcal{Z} \rightarrow \mathcal{X}
D: \mathcal{X} \rightarrow [0,1]
\arg\!\min_{G}\max_{D} \mathbb{E}_{X\sim p_{data}} [\log{D(X)}] + \mathbb{E}_{Z\sim p_Z}[\log{(1-D(G(Z))}]
A Review on GANs and Adversarial Training
Adversarial Domain Adaptation
Ref: Adversarial Discriminative Domain Adaptation - Tzeng et al. 2017
Adversarial Domain Adaptation
Idea: Consider a intermediate feature space, a common representation for both domains.
Train a classifier on the labeled source data, passing through the representation space
Play a game between the maps and a discriminator
M_s: \mathcal{X}_s \rightarrow \mathcal{R}
M_t: \mathcal{X}_t \rightarrow \mathcal{R}
C: \mathcal{R} \rightarrow \mathcal{Y}
D: \mathcal{R} \rightarrow [0,1]
Adversarial Domain Adaptation
Pre-training:
\min\limits_{M_s, C} - \mathbb{E}_{P_s} \left[ \sum\limits_{k=1}^K \mathbb{1}_{[y_s = k]} \log C\left( M_s(x_s) \right)\right]
Adversarial Domain Adaptation
Adversarial Adaptation (First turn)
\min\limits_{D} - \mathbb{E}_{P_s} \left[ \log D\left( M_s(x_s) \right) \right] - \mathbb{E}_{P_t} \left[ \log\left(1 - D\left( M_t(x_t)\right) \right) \right]
Adversarial Domain Adaptation
Adversarial Adaptation (Second turn)
\min\limits_{M_t} - \mathbb{E}_{P_t} \left[ \log D\left( M_t(x_t) \right) \right]
deck
By Daniel Yukimura
