Fangyu Wu

Computer Science

1. Expression Recognition 100%
2. Models 80%
3. Convolutional Neural Network 72%
4. Reidentification 61%
5. Semisupervised Learning 61%
6. Facial Expression 61%
7. Face Recognition 61%
8. Generative Adversarial Networks 53%
9. Language Modeling 46%
10. Embedding 43%
11. Experimental Result 37%
12. Vectors 36%
13. Benchmark 36%
14. Application 36%
15. Domains 35%
16. Weighting Function 30%
17. Objective Function 30%
18. Image Translation 30%
19. maximum-likelihood 30%
20. Few-Shot Learning 30%
21. Average Utility 30%
22. Sentiment Analysis 30%
23. Likelihood Estimation 30%
24. Multi-Classifiers 30%
25. Descriptors 30%
26. Unsupervised Domain Adaptation 30%
27. Training Sample 30%
28. Reinforcement Learning 30%
29. multiple classifier 30%
30. Slow Convergence 30%
31. Sequence Model 30%
32. Deep Convolutional Neural Networks 30%
33. Pose Variation 30%
34. Text Classification 30%
35. Contrastive Learning 30%
36. Embedding 30%
37. Attention (Machine Learning) 30%
38. Visual Language 30%
39. Wavelet Transforms 30%
40. Feature Fusion 30%
41. Discriminative Feature 30%
42. Real World 30%
43. Computer Vision 29%
44. Database 28%
45. Algorithms 25%
46. Feature Extraction 25%
47. Learned Feature 20%
48. Rapid Development 20%
49. Semantics 20%
50. Accuracy 20%