AI Roadmap

1. Better Perception & Understanding

1.1. Residual concept

1.2. Attention-based model

1.3. Reasoning

1.3.1. Graph networks

2. Generation & Creativity

2.1. Implicit generative models

2.2. Image-to-Image

2.3. Program induction

2.4. AutoEncoder

2.5. Factor disentanglement

3. Model Interpretation

3.1. Data/Feature Visualization

3.2. XAI (Explainable AI)

4. Safety & Privacy

4.1. Adversarial examples

4.2. Training from private samples

5. Etc.

5.1. various sensor

5.1.1. RGB, Depth, IR, LiDAR

5.2. Learned Index Structures

6. Bioinformatics

6.1. Genomics

6.2. Medical image

6.3. U-Healthcare

7. Autonomous

7.1. AutoML

7.2. Meta learning

8. Resource Management

8.1. Student-teacher

8.2. Model compression

8.3. Asynchronous algorithm

8.4. Data augmentation

9. Control in RL

9.1. Imitation learning

9.2. Inverse RL

10. Human related

10.1. Active learning

10.2. Human in the loop

10.3. Crowd sourcing

11. General Topic

11.1. Overfitting

11.2. Class imbalance