Academic Poster Content (With References)

1. Title

"Exploring the Architecture and Application of Transformer Models in NLP and Media Generation"

Authors: Zhairui Shen, Tianwei Wang

Institution: Arcadia University, Glenside, PA, USA

2. Introduction

Introduction to the impact of Transformer models in NLP and AI-driven content generation. Highlights the significant advances in generating images and videos from textual descriptions using Transformers and GANs.

Reference: Vaswani, A., Shazeer, N., Parmar, N., et al. "Attention Is All You Need." NeurIPS 2017 [1].

3. Research Objectives

Outline the goals of optimizing Transformer architecture:

• To enhance the precision and contextual accuracy of visual outputs.
• To improve training efficiency without sacrificing accuracy.

Reference: Brown, T. B., Mann, B., Ryder, N., et al. "Language Models are Few-Shot Learners." NeurIPS 2020 [2].

4. Methodology

4.1 Transformer Model Components

Detailed breakdown of key Transformer components:

• Self-Attention Mechanisms: Attention allows the model to consider different parts of the input simultaneously, enhancing the understanding of complex textual patterns.
• Reference: Vaswani, A., et al. (2017) [1].
• Positional Encoding: Positional encoding is used to provide the model with information about the position of words in a sequence, enabling the model to understand the order of words.
• Reference: Gitconnected. "Understanding Transformers from Start to End: A Step-by-Step Math Example." [11].

4.2 Mixed Precision Training for Acceleration

Mixed precision training effectively balances computational efficiency and training accuracy.

Reference: Micikevicius, P., Narang, S., Alben, J., et al. "Mixed Precision Training." ICLR 2018 [3].

4.3 Distributed Training

Distributed training using multiple GPUs helps to overcome the computational challenges posed by large Transformer models.

Reference: Goyal, P., Dollár, P., Girshick, R., et al. "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour." CVPR 2018 [4].

5. Model Improvement Strategies

5.1 Attention Mechanism Refinement

Investigation into sparse attention mechanisms to enhance computational efficiency.

Reference: Child, R., Gray, S., Radford, A., et al. "Generating Long Sequences with Sparse Transformers." arXiv 2019 [5].

5.2 Model Pruning, Quantization, and Knowledge Distillation

Enhancements to model architecture using pruning and quantization.

Reference: Han, S., Pool, J., Tran, J., et al. "Learning both Weights and Connections for Efficient Neural Networks." NeurIPS 2015 [6].

6. Applications in AI-Driven Media Generation

6.1 Text-to-Image and Text-to-Video Generation

Descriptions of generating images and videos from textual descriptions using GANs and Transformers.

Reference: Ramesh, A., Pavlov, M., Goh, G., et al. "Zero-Shot Text-to-Image Generation." ICML 2021 [7].

Reference: Koh, J., Park, S., Song, J. "Improving Text Generation on Images with Synthetic Captions." arXiv 2024 [12].

7. Training Optimization Techniques

7.1 Optimized Memory Access and Operator Fusion

Use of memory access optimizations and operator fusion to increase computational efficiency.

Reference: Jia, Y., Shelhamer, E., Donahue, J., et al. "Caffe: Convolutional Architecture for Fast Feature Embedding." MM 2014 [8].

8. Results and Discussion

Analysis of improvements made to the original Transformer model:

• Quantitative Analysis: Comparing FID and IS before and after applying sparse attention and pruning.

Reference: Heusel, M., Ramsauer, H., Unterthiner, T., et al. "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium." NeurIPS 2017 [9].

9. Conclusion and Future Work

Future expansion of Transformer models to other creative domains such as music and 3D modeling.

Reference: Dhariwal, P., Nichol, A. "Diffusion Models Beat GANs on Image Synthesis." NeurIPS 2021 [10].

10. References

1. Vaswani, A., Shazeer, N., Parmar, N., et al. "Attention Is All You Need." NeurIPS, 2017.
2. Brown, T. B., Mann, B., Ryder, N., et al. "Language Models are Few-Shot Learners." NeurIPS, 2020.
3. Micikevicius, P., Narang, S., Alben, J., et al. "Mixed Precision Training." ICLR, 2018.
4. Goyal, P., Dollár, P., Girshick, R., et al. "Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour." CVPR, 2018.
5. Child, R., Gray, S., Radford, A., et al. "Generating Long Sequences with Sparse Transformers." arXiv, 2019.
6. Han, S., Pool, J., Tran, J., et al. "Learning both Weights and Connections for Efficient Neural Networks." NeurIPS, 2015.
7. Ramesh, A., Pavlov, M., Goh, G., et al. "Zero-Shot Text-to-Image Generation." ICML, 2021.
8. Jia, Y., Shelhamer, E., Donahue, J., et al. "Caffe: Convolutional Architecture for Fast Feature Embedding." MM, 2014.
9. Heusel, M., Ramsauer, H., Unterthiner, T., et al. "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium." NeurIPS, 2017.
10. Dhariwal, P., Nichol, A. "Diffusion Models Beat GANs on Image Synthesis." NeurIPS, 2021.
11. Gitconnected. "Understanding Transformers from Start to End: A Step-by-Step Math Example." Link.
12. Koh, J., Park, S., Song, J. "Improving Text Generation on Images with Synthetic Captions." arXiv, 2024.