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Intro
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Classroom Contents
Making Our Models Robust to Changing Visual Environments
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- 1 Intro
- 2 Benchmark Performance
- 3 Dataset Bias
- 4 Classic Domain Adaptation
- 5 Deep Domain Adaptation
- 6 Discrepancy Between Source and Target
- 7 Domain Adversarial Optimization
- 8 Domain Adversarial Adaptation
- 9 Standard GAN Model
- 10 CycleGAN for Domain Adaptation
- 11 Failures of Image to Image Translation
- 12 Adaptation Results: Digit Recognition
- 13 Adaptation of Semantic Segmentation
- 14 Cross-city Adaptation
- 15 Cross Season Adaptation
- 16 Cross Season Pixel Adaptation
- 17 Synthetic to Real Pixel Adaptation
- 18 Summary: Adversarial Domain Adaptation
- 19 Continuous Learning
- 20 Continuous Unsupervised Adaptation
- 21 Experiment: MNIST Rotations
- 22 Replay to Remember: MNIST Rotations
- 23 Adapt vs Remember: MNIST Rotations
- 24 Evaluate MNIST 135 after all rotations
- 25 Summary Batch Adaptation
- 26 Summary Continuous Adaptation
