domain adaptation for semantic segmentation via class ... · use synthetic data to obtain infinite...
TRANSCRIPT
![Page 1: Domain Adaptation for Semantic Segmentation via Class ... · Use Synthetic Data to Obtain Infinite GTs? Original image from GTA5 Ground truth from game Engine Original image from](https://reader030.vdocuments.us/reader030/viewer/2022041302/5e11d60aa6c04e7cad26cefb/html5/thumbnails/1.jpg)
Confidence Regularized Self-Training
Yang Zou Zhiding Yu Xiaofeng Liu Vijayakumar
Bhagavatula
Jinsong Wang
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Source Domain (Labeled) Target Domain (Unlabeled)
Image
classification
Unsupervised Domain Adaptation (UDA)
Semantic
segmentation
Car
Adaptation
Adaptation
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Target Images (Cityscapes)
Deep
CNN
Source Labels (GTA5)
Source Images (GTA5)
Predictions (Cityscapes)
Pseudo-labels (Cityscapes)
GTA5 → Cityscapes
Yang Zou, Zhiding Yu et al., Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training, ECCV18
UDA through Iterative Deep Self-Training
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where: x: input sample p: class predication vector y: label vector !": pseudo-label vector
w: network parameters s: source sample index t: target sample index △$%&: probability simplex
Pseudo-label
generation
Network
retraining
Car
Person
Bus
Balanced softmax Pseudo-label !"∗ Network output
after self-training
Class-Balanced Self-Training (CBST)
Yang Zou, Zhiding Yu et al., Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training, ECCV18
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Samples from VisDA-17 (With label “Car”)
Building
Person
Building
Sky
Building
Sample from BDD100K
Green: Correct Red: Misclassified
Preventing Overconfidence in Self-Training
Pseudo-label
Label
regularization
Model
regularization
Network output after
self-training
Pseudo-label
generation
Image label: “Car”
Network output before
self-training
Network
retraining
(b) Label regularized self-training
(c) Model regularized self-training
(a) Self-training without confidence regularization
Backbone
Network
Car
Person
Bus
Network
retraining
Network
retraining
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Car
Person
BusPseudo-label
generation
Network
retraining
where: !: regularizer weight
Label Regularized Self-Training (LR)
Balanced softmax Pseudo-label "#∗ Network output
after self-training
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Car
Person
BusPseudo-label
generation
Network
retraining
where: !: regularizer weight
Model Regularized Self-Training (MR)
Balanced softmax Pseudo-label "#∗ Network output
after self-training
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LR-Entropy (LRENT) MR-KLDiv (MRKLD)
MR-Entropy (MRENT)
MR-L2 (MRL2)
Pseudo-label solver
Regularized retraining loss
v.s. probability
Pseudo-label generation loss
v.s. probability
Proposed Confidence Regularizers
0 0.25 0.5 0.75 1y
0
1
2
3
regula
rize
d lo
ss
LRNET
=0=0.5=1
y*
0 0.25 0.5 0.75 1p
-10
2
4
6
8
10
regula
rize
d lo
ss
MRENT
=0=0.5=1
p*
0 0.25 0.5 0.75 1p
-10
2
4
6
8
10
regula
rize
d lo
ss
MRKLD
=0=0.5=1
p*
0 0.25 0.5 0.75 1p
-10
2
4
6
8
10
regula
rize
d lo
ss
MRL2
=0=0.5=1
p*
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Probabilistic Explanation
Proposition 1. CRST can be modeled as a regularized maximum likelihood for classification (RCML) problem
optimized via classification expectation maximization.
Theoretical Analysis
Proposition 2. Given pre-determined !"′s, CRST is convergent with gradient descent for network retraining
optimization.
Proposition 4. Self-training with MRKLD is equivalent to self-training with pseudo-label uniformly smoothed
by $ =(Kα−α)/(K+Kα), where α is the regularizer weight.
Proposition 3. If !" are equal for all k, the soft pseudo-label of LRENT given is exactly the same as softmax
with temperature
Convergence Analysis
Softlabel and Softmax with temperature
Label smoothing
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Results on VisDA-17
Results on Office-31
Experiment: UDA for Image Classification
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Results on SYNTHIA -> Cityscapes (mIoU* - 13 class)
Results on GTA5 -> Cityscapes
Experiment: UDA for Semantic Segmentation
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Original Image Ground Truth Source Model CBST CRST (MRKLD)
Experiment: Qualitative Results (GTA5 -> Cityscapes)
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Conclusions
§ Compared with supervised learning, self-training is an under-determined problem (EM with latent variables).
§ Our work shows the importance of confidence regularizations as inductive biases to help under-constrained
problems such as unsupervised domain adaptation and semi-supervised learning.
§ CRST is still aligned with entropy minimization. The proposed confidence regularization only serves as a safety
measure to prevent over self-training/entropy minimization.
§ MR-KLD is most recommended in practice for its efficiency and good performance.
Future Works
§ This work could potentially inspire many other meaningful regularizations/inductive biases for similar problems.
Conclusions and Future Works