Transfer Learning - Getting Started

Transfer LearningTranser Learning

• The previous layer, surface features (representable); the latter layer deep features (not characterization)
• Through the discriminator, the real and fake are forced to be similar

• Developed from Generative Adversarial Networks

Transfer: learn from source domain – put to use in target domain

• Why migrate?

Intersection between source and target domains! commom features

Adaptation: traditional loss function, feature adaptation, model adaptation

1. based on error
2. Based on confrontation
3. Based on reconstruction (Cycle GAN)

• BYOL self-supervised model

Fine-tuning

• The target domain generally has a small amount of data, and the network is prone to overfitting

Therefore, ft is the model trained in the source domain, just put it in the target domain and fine-tune it.
Note: The target domain must also be trained!

Prevent overfitting: the first few layers and the next few layers are fixed, and only the middle layer is adjusted

Which layer should be fine-tuned?

• What does tuning mean: Manually adjust the weight of that layer, and then fix it (without backpropagation update), other layers backpropagation update

Speech: adjust the next few layers
NLP: the first few layers

• What if the source domain has labels but the target domain does not? (Target domain is unlabeled training set and labeled test set)

1. DDC

   • m samples in the source domain and n labels in the target domain are respectively calculated as MMD (maximum mean difference), does it look like
   • The effect is not good

2. AND

3. RTN : Residual Transfer Network

   • Enter the target domain, the first few layers are relatively close
   • According to different tasks in the target domain, there are different branches, and the classified branches are added to the residual

4. RE-Gard gradient reversal

   • input – CNN – feature -> classification emphasizes fully connected network
   • Put it in the target domain, the effect is not good
   • Solution : add one 域判别器, the feature of the source domain corresponds to - domain 1, and the feature corresponding to the target domain corresponds to - domain 0
   • Use a network, add a branch of domain discrimination, and the two branches fight against each other
   • gradient reversal layer
   • The ultimate goal is to make the classifier unclear whether the feature comes from the source domain or the target domain

5. MADA

   • Problem: There are 3 classes in the source domain and 2 classes in the target domain, MMD is too simple and the effect is not good
   • Add discriminators : Number of categories in target domain = Number of domain discriminators
   • Don't know how to classify the labels of the target domain?
     • Pre-discriminate in the classifier of the source domain, not only the domain alignment, but also the class alignment

zero-shot learning