Homework 11 - Transfer Learning (Domain Adversarial Training)
Author: Arvin Liu (r09922071@ntu.edu.tw)
If there are any questions, please contact mlta-2022-spring@googlegroups.com
Readme
In homework 11, you will need to implement Domain Adversarial Training in Transfer Learning. As shown in the bottom left part of the figure.
Scenario and Why Domain Adversarial Training
Now we have labeled source data and unlabeled target data, where source data might be relavent to the target data. We now want to train a model with source data only and test it on target data.
What problem might occur if we do so? After we have learned Anomaly Detection, we now know that if we test the model with an abnormal data that have never appeared in source data, our trained model is likely to result in poor performance since it is not familiar with the abnormal data.
For example, we have a model that contains Feature Extractor and Classifier:
When the model is trained with source data, the feature extractor will extract meaningful features since it is familiar with the distribution of it.It could be seen in the following figure that the blue dots, which is the distribution of source data, has already been clustered into different clusters. Therefore, the Classifier can predict the label based on these clusters.
However, when test on the target data, the Feature Extractor will not be able to extract meaningful features that follow the distribution of the source feature distribution, which result in the classifier learned for the source domain will not be able to apply to the target domain.
Domain Adversarial Training of Nerural Networks (DaNN)
Based on the above problems, DaNN approaches build mappings between the source (training-time) and the target (test-time) domains, so that the classifier learned for the source domain can also be applied to the target domain, when composed with the learned mapping between domains.
In DaNN, the authors added a Domain Classifier, which is a deep discriminatively-trained classifeir in the training framework to distinguish the data from different domain by the features extracted by the feature extractor. As the training progresses, the approach promotes a domain classifier that discriminates between the source and the target domains and a feature extractor that can extractor features that are discriminative for the main learning task on the source domain and indiscriminate with respect to the shift between the domains.
The feature extractor are likely to outperform the domain classifier as its input are generated by the feature extractor and that the task of domain classification and label classification are not conflict.
This method leads to the emergence of features that are domain-invariant and on the same feature distribution.
Data Introduce
Our task contains source data: real photos, and target data: hand-drawn graffiti.
We are going to train the model with the photos and the labels, and try to predict what the labels are for hand-drawn graffiti.
The data could be downloaded here. The code below is for data downloading and visualization.
Note that: The source and target data are all balanced data, you can make use of this information.
--2024-04-28 12:10:16-- https://github.com/redxouls/ml2020spring-hw11-dataset/releases/download/v1.0.1/real_or_drawing.zip Resolving ga.dp.tech (ga.dp.tech)... 10.255.254.37, 10.255.254.18, 10.255.254.7 Connecting to ga.dp.tech (ga.dp.tech)|10.255.254.37|:8118... connected. Proxy request sent, awaiting response... 302 Found Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/465172560/4bd531a0-543b-4d65-b910-b7b20d404c0a?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240428%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240428T041017Z&X-Amz-Expires=300&X-Amz-Signature=85b3a49f7b6b4416a378f67b01ad4fb76e64dbd1203ac59eddf75d0259fa5987&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=465172560&response-content-disposition=attachment%3B%20filename%3Dreal_or_drawing.zip&response-content-type=application%2Foctet-stream [following] --2024-04-28 12:10:17-- https://objects.githubusercontent.com/github-production-release-asset-2e65be/465172560/4bd531a0-543b-4d65-b910-b7b20d404c0a?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAVCODYLSA53PQK4ZA%2F20240428%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20240428T041017Z&X-Amz-Expires=300&X-Amz-Signature=85b3a49f7b6b4416a378f67b01ad4fb76e64dbd1203ac59eddf75d0259fa5987&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=465172560&response-content-disposition=attachment%3B%20filename%3Dreal_or_drawing.zip&response-content-type=application%2Foctet-stream Connecting to ga.dp.tech (ga.dp.tech)|10.255.254.37|:8118... connected. Proxy request sent, awaiting response... 200 OK Length: 107293358 (102M) [application/octet-stream] Saving to: ‘real_or_drawing.zip’ real_or_drawing.zip 100%[===================>] 102.32M 11.1MB/s in 8.8s 2024-04-28 12:10:27 (11.7 MB/s) - ‘real_or_drawing.zip’ saved [107293358/107293358] Archive: real_or_drawing.zip creating: real_or_drawing/ creating: real_or_drawing/test_data/ creating: real_or_drawing/test_data/0/ inflating: real_or_drawing/test_data/0/00000.bmp inflating: real_or_drawing/test_data/0/00001.bmp inflating: real_or_drawing/test_data/0/00002.bmp inflating: real_or_drawing/test_data/0/00003.bmp inflating: real_or_drawing/test_data/0/00004.bmp inflating: real_or_drawing/test_data/0/00005.bmp inflating: real_or_drawing/test_data/0/00006.bmp inflating: real_or_drawing/test_data/0/00007.bmp inflating: real_or_drawing/test_data/0/00008.bmp inflating: real_or_drawing/test_data/0/00009.bmp inflating: real_or_drawing/test_data/0/00010.bmp inflating: 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Special Domain Knowledge
When we graffiti, we usually draw the outline only, therefore we can perform edge detection processing on the source data to make it more similar to the target data.
Canny Edge Detection
The implementation of Canny Edge Detection is as follow. The algorithm will not be describe thoroughly here. If you are interested, please refer to the wiki or here.
We only need two parameters to implement Canny Edge Detection with CV2: low_threshold
and high_threshold
.
cv2.Canny(image, low_threshold, high_threshold)
Simply put, when the edge value exceeds the high_threshold, we determine it as an edge. If the edge value is only above low_threshold, we will then determine whether it is an edge or not.
Let's implement it on the source data.
Data Process
The data is suitible for torchvision.ImageFolder
. You can create a dataset with torchvision.ImageFolder
. Details for image augmentation please refer to the comments in the following codes.
Model
Feature Extractor: Classic VGG-like architecture
Label Predictor / Domain Classifier: Linear models.
Pre-processing
Here we use Adam as our optimizor.
Start Training
DaNN Implementation
In the original paper, Gradient Reversal Layer is used. Feature Extractor, Label Predictor, and Domain Classifier are all trained at the same time. In this code, we train Domain Classifier first, and then train our Feature Extractor (same concept as Generator and Discriminator training process in GAN).
Reminder
- Lambda, which controls the domain adversarial loss, is adaptive in the original paper. You can refer to the original work . Here lambda is set to 0.1.
- We do not have the label to target data, you can only evaluate your model by uploading your result to kaggle.:)
Inference
We use pandas to generate our csv file.
BTW, the performance of the model trained for 200 epoches might be unstable. You can train for more epoches for a more stable performance.
Visualization
We use t-SNE plot to observe the distribution of extracted features.
Step1: Load checkpoint and evaluate to get extracted features
Step2: Apply t-SNE and normalize
Step3: Visualization with matplotlib
Training Statistics
Number of parameters:
- Feature Extractor: 2, 142, 336
- Label Predictor: 530, 442
- Domain Classifier: 1, 055, 233
Simple
Training time on colab: ~ 1 hr
Medium
Training time on colab: 2 ~ 4 hr
Strong
Training time on colab: 5 ~ 6 hrs
Boss
Unmeasurable
Learning Curve (Strong Baseline)
- This method is slightly different from colab.
Accuracy Curve (Strong Baseline)
- Note that you cannot access testing accuracy. But this plot tells you that even though the model overfits the training data, the testing accuracy is still improving, and that's why you need to train more epochs.
Q&A
If there is any problem related to Domain Adaptation, please email to b08901058@ntu.edu.tw / mlta-2022-spring@googlegroups.com。