using CycleGAN to address domain shift and fine-tuning a classifier on the adapted dataset is a highly effective method for bridging gaps between source and target domains
Why?
reduce the domain gap
Artistic Style to Photographic Images
Goal - Reduce the Domain Shift enough to be able to classificy the target domain images with just fine-tunning a model
CycleGAN
It consists of two generators (G and F) and two discriminators (D_X and D_Y). The goal is to learn mappings between two domains, X → Y and Y → X, without needing paired examples
Models
need new definations
The generator translates images from one domain to another. The discriminator classifies whether an image is real or generated.
CycleGAN Training Loop
The training loop for CycleGAN consists of alternating updates between two generator networks and two discriminator networks.
Epoch 1/10 - Loss_G: 6.5575, Loss_F: 6.3069, Loss_D_A: 0.0754, Loss_D_B: 0.2460 Epoch 2/10 - Loss_G: 7.1973, Loss_F: 6.9931, Loss_D_A: 0.2243, Loss_D_B: 0.3103 Epoch 3/10 - Loss_G: 8.0973, Loss_F: 7.7318, Loss_D_A: 0.1714, Loss_D_B: 0.2557 Epoch 4/10 - Loss_G: 8.9121, Loss_F: 8.6646, Loss_D_A: 0.2167, Loss_D_B: 0.2153 Epoch 5/10 - Loss_G: 8.0969, Loss_F: 7.7281, Loss_D_A: 0.0644, Loss_D_B: 0.2283 Epoch 6/10 - Loss_G: 10.6232, Loss_F: 10.4350, Loss_D_A: 0.0238, Loss_D_B: 0.2547 Epoch 7/10 - Loss_G: 11.3655, Loss_F: 10.8175, Loss_D_A: 0.0340, Loss_D_B: 0.3024 Epoch 8/10 - Loss_G: 9.3688, Loss_F: 8.8938, Loss_D_A: 0.0866, Loss_D_B: 0.2154 Epoch 9/10 - Loss_G: 9.2278, Loss_F: 8.6637, Loss_D_A: 0.0244, Loss_D_B: 0.3427 Epoch 10/10 - Loss_G: 7.6658, Loss_F: 7.4510, Loss_D_A: 0.0197, Loss_D_B: 0.2439
Translate Source Domain Images
Utilize the trained generators to convert all source domain images into the style of the target domain. The resulting adapted dataset will then serve as a foundation for downstream tasks such as classification.
Fine-Tune for Classification
Once the source domain images have been adapted to match the target domain's distribution, fine-tune a classification model using the new source domain images
Epoch [1/10], Loss: 3.6808 Epoch [2/10], Loss: 2.6575 Epoch [3/10], Loss: 1.6770 Epoch [4/10], Loss: 1.1055 Epoch [5/10], Loss: 0.5429 Epoch [6/10], Loss: 0.1904 Epoch [7/10], Loss: 0.1130 Epoch [8/10], Loss: 0.0803 Epoch [9/10], Loss: 0.0517 Epoch [10/10], Loss: 0.0544
Conclusion
The steady decline in training loss indicates strong model convergence with the adapted dataset loader, and with proper validation, this workflow can achieve excellent generalization on the target domain. By tackling domain shift creatively and efficiently, you're enhancing model robustness for real-world computer vision applications.