<table><caption>Table 4.6: Accuracy, balanced accuracy and mean absolute angle error of the MPSN model predictions on map 1, the reduction is in terms of number of queries.</caption><thead><tr><th rowspan="2">κ</th><th rowspan="2">reduction</th><th rowspan="2">accuracy</th><th rowspan="2">balanced accuracy</th><th colspan="3">maae (degrees)</th></tr><tr><th>φ<sub>1</sub></th><th>Φ</th><th>φ<sub>2</sub></th></tr></thead><tbody><tr><td>0.6</td><td><strong>99.58%</strong></td><td>95.7%</td><td>55.9%</td><td>10.0</td><td>5.3</td><td>7.2</td></tr><tr><td>0.7</td><td>98.59%</td><td>97.5%</td><td>63.5%</td><td>5.7</td><td>3.2</td><td>4.7</td></tr><tr><td>0.8</td><td>94.70%</td><td>98.4%</td><td>75.7%</td><td>3.6</td><td>2.1</td><td>3.2</td></tr><tr><td>0.9</td><td>66.54%</td><td><strong>99.4%</strong></td><td><strong>88.8%</strong></td><td><strong>1.3</strong></td><td><strong>0.8</strong></td><td><strong>1.2</strong></td></tr></tbody></table>

<table><caption>Table 4.7: Accuracy, balanced accuracy and mean absolute angle error of the MPSN model predictions on map 100T, the reduction is in terms of number of queries.</caption><thead><tr><th rowspan="2">κ</th><th rowspan="2">reduction</th><th rowspan="2">accuracy</th><th rowspan="2">balanced accuracy</th><th colspan="3">maae (degrees)</th></tr><tr><th>φ<sub>1</sub></th><th>Φ</th><th>φ<sub>2</sub></th></tr></thead><tbody><tr><td>0.6</td><td><strong>99.50%</strong></td><td>99.2%</td><td>66.6%</td><td>50.9</td><td>39.3</td><td>51.0</td></tr><tr><td>0.7</td><td>97.84%</td><td>99.3%</td><td>73.3%</td><td>22.4</td><td>18.1</td><td>24.3</td></tr><tr><td>0.8</td><td>92.37%</td><td>99.6%</td><td>87.9%</td><td>11.3</td><td>9.0</td><td>12.8</td></tr><tr><td>0.9</td><td>65.67%</td><td><strong>99.9%</strong></td><td><strong>95.2%</strong></td><td><strong>4.8</strong></td><td><strong>3.9</strong></td><td><strong>5.8</strong></td></tr></tbody></table>

These results show that the MPSN model architecture can drastically reduce the cost of analyzing TEM data with deterministic approaches by over 90%, while providing a high accuracy on the resulted MAPS after the analysis. Furthermore, we show from our experimental protocol that the model can generalize to new experimental TEM data without the need of training on simulated data or fine-tuning. These arguments support the realistic deployment of such a DL approach in an interactive setting with an existing deterministic method to significantly speed up The TEM data analysis for a real-time use case.
<figure><img src="image_50.png" /><figcaption>(a) Phases and Euler's orientation maps of micrograph of map 1.</figcaption></figure>

<figure><img src="image_51.png" /><figcaption>(b) Predicted phases and Euler's orientation maps using the MPSN model with $\kappa = 0.8$.</figcaption></figure>

Figure 4.14: Phases and Euler's orientation maps found using the segmentation map predicted by the MPSN model on map 1.
<figure><img src="image_52.png" /><figcaption>(b) Predicted phases and Euler's orientation maps using the MPSN model with $\kappa = 0.8$.</figcaption></figure>

Figure 4.15: Phases and Euler's orientation maps found using the segmentation map predicted by the MPSN model on map 100T.
## 4.4 Conclusion

In this work, we investigated the potential of deep learning for the rapid automation of TEM data analysis. We proposed a preprocessing of simulated and experimental DPs with descriptors. We demonstrated the value of multi-task learning for the regression task of orientation prediction and the classification task of phase determination and transfer learning by successfully learning to analyze simulated TEM diffraction data and applying it to experimental diffraction data. In parallel, we performed an extensive comparative study of SOTA DL models to analyze diffraction data with a realistic experimental protocol. Furthermore, we introduced the segmentation map a new visualization support to allow DL-based solutions to interact in an interactive way with existing deterministic approaches for TEM data analysis. In this context, we address the problem of TEM data analysis for DL as the task of differentiating between experimental DPs, in order to predict the corresponding segmentation map. We proposed a new DL model, namely the Multi-task Pairwise Siamese Network with dedicated training and inference procedures. The model is able to drastically reduce labeling cost in terms of the number of queries of traditional deterministic algorithms for TEM analysis while showing favorable results in the prediction of maps using label propagation on the predicted segmentation map with promising performance in generalizing to unseen micrographs.

In the current state of the presented methods, it appears that accurate phase determination can be reached both based on training on simulated images and directly on experimental data. In the latter case, the main issue is obviously the amount of data available and the diversity of data, since experimental maps contain a large proportion of duplicates. As for the simulated data strategy, in order to become more user-friendly, future work should also address how a pre-trained model could be easily transferred to different experimental configurations (camera length, precession conditions, etc.). Concerning the prediction of orientation angle, promising precision has been obtained on some Euler angles but not all, and further research is needed to improve this precision to a degree where the noise on experimental maps can be reduced to observe relevant features in most microstructures. The possibility to implement a real time approximation of the phase and orientation map during TEM acquisition is not out of reach given the results presented here, and would be a valuable asset for more useful data collection by the experimentalists.
# Chapter 5

## Conclusions and Future Perspectives

The work in this thesis has concentrated on demonstrating the relevance of implicit information in the design of machine learning algorithms. This study direction has been extensively investigated by the community, with its different subdomains in semi-supervised learning, active learning, and transfer learning. We demonstrated that implicit information may take numerous forms and is especially useful in learning circumstances where labeled information is sparse and expensive. This implicit information can also come from a variety of sources, such as data distribution, a learning algorithm's knowledge model, or simulations. We studied these many sources of implicit information by developing tailored learning algorithms to recognize and exploit this knowledge to improve learning efficiency.

Further research into new approaches to develop learning algorithms to recognize and apply implicit information is critical. To solve our daily activities, we create effective ways of combining contextual knowledge and prior experiences. However, without enough labeled data, these activities remain beyond the capabilities of machine intelligence.

Future goals should be to achieve human intelligence not by completing a specific set of problems, as was previously regarded to be a milestone for artificial intelligence, but rather by developing learning models and algorithms that better blend memory, experience, and perception. With developing study paths such as few-shot learning or self-learning, the research community has already set its course to move in this direction. In my opinion, the research done in this area will be important in advancing AI to the next level.
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