Textual Style Transfer: Rewriting Without Parallel Data
Module: Natural Language Processing | Difficulty: Advanced
Style Transfer
Non-Parallel Approach
- Encode content and style separately
- Decode with target style
Content Preservation
Style Accuracy
Trade-off
import torch
import torch.nn as nn
class StyleTransferModel(nn.Module):
def __init__(self, encoder, decoder, style_classifier):
super().__init__()
self.content_encoder = encoder
self.style_classifier = style_classifier
self.decoder = decoder
def forward(self, x, target_style):
content = self.content_encoder(x)
output = self.decoder(content, target_style)
return output
def loss(self, x, target_style, original_style):
content = self.content_encoder(x)
output = self.decoder(content, target_style)
style_loss = nn.functional.cross_entropy(self.style_classifier(output), target_style)
content_loss = nn.functional.mse_loss(self.content_encoder(output), content)
return style_loss + content_loss
Research Insight: Non-parallel style transfer is challenging because there is no direct supervision. The key insight is to disentangle content and style representations, then recombine them with the target style. VAE-based approaches achieve this disentanglement through the KL divergence penalty.