What is the best way to train artificial intelligence models?

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Authors:

(1) Hyeongjun Kwon, Yonsei University;

(2) Jinhyun Gang, University of Yunusi;

(3) Jin Kim, University of Uniony;

(4) Kwonyoung Kim, Yonsei University;

(5) Kwanghoon Sohn, Yonsei University and the Korea Institute of Science and Technology (KIST).

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Links table

Abstract and 1 introduction

2. Related work

3. Excess engineering

4. The method

4.1. summary

4.2. Tree of possibly hierarchy

4.3. The optical hierarchy analyzes

4.4. Learn the hierarchy in the Zaidi space

4.5. Optical hierarchy coding

5. Experiences and 5.1. Classification of photos

5.2. Discovering organisms and retail the similar

5.3. Semantic

5.4. Perception

6. Huritage and discussion studies

7. Conclusion and references

A. Network structure

Theoretical foundation line

Additional results

D. Additional visualization

Additional results

C.1. Good refinement for full training.

We also verify the effectiveness of our proposed method when applying it to training the model from the zero point. For fair comparisons, we evaluate the performance of the Hi-Mapper trained rating with Full training Plan (350 Ages) and fine tuning Plan (baseline + 50 era) of the same learning goals on imagenet-1K [36]. As shown in the tab. 6, experimental results show that fine tuning The plan is better than Full training In terms of understanding the structural organization of the visual scenes.

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D. Additional visualization

For a more comprehensive understanding, we will provide additional visualization results that are included in the main paper and also the optical hierarchy examination in CNNS [59]As shown in Figure 7, 8. This will present an insight into the aspects of representation of features in transformer structures and CNN, as well as the benefits of applying our way.

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::: Information about this paper Available on Arxiv Under CC by 4.0 verb license.

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