Domain Adaptation
ML 2019 Fall Final Prj
~Use real images to predict drawing~
Arvin Liu
What is Domain Adaptation?
Domain Adaptation
Domain Adaptation in the Task
Real Image -> Drawing
(32 x 32 x 3)
(28 x 28 x 1)
Domain Adaptaion
有Label
沒有Label
Feature Extractor
Classifier
Source
Target
Source: blue
Target: Red
Feature Distribution
Why Need
Domain Adaptation
Data Format
Classes
- 0 - Cow
- 1 - Bed
- 2 - Clock
- 3 - Apple
- 4 - Cat
- 5 - Airplane
- 6 - Television
- 7 - Dog
- 8 - Dolphin
- 9 - Spider
Input Format (balanced)
- trainX.npy
np.load("trainX.npy")
# Get (5000, 32, 32, 3)- test.npy
np.load("test.npy")
# Get (100000, 28, 28, 1)- trainY.npy
np.load("trainY.npy")
# Get (5000,)(label = 0~9)
(Balanced)
(Balanced)
Output Format
id,label
0,0
1,0
2,0
3,0
4,0
5,0
6,0就是同你的Hw3
Evaluation Metrices
Just Top 1 Accuracy.
Policies
No Human Learning/Labeling
No Extra Dataset
Allowed Pre-trained Model
Only torchvision's pre-trained models are allowed.
Kaggle Link
Simple Baseline
public score - 0.47103
如果狀況太差的話會調降，只會降: )
Timeline
GUIDE
Guide
- Edge Detection
- DANN
- MCD
Edge Detection
Domain Adaptation?
Edge
Detection
直接讓Train很像Test也是種"Adaptation"？
*直接用cv2套件就可以了 (cv2.Canny)
Actually...
這邊就有一個提示了XD
下圖是上圖的邊緣偵測結果