Phase 1: Research and Development
Objective: Lay the groundwork for the text-to-image generation AI.
1. AI Architecture Design and Development:
Define neural network architecture, exploring GANs, transformer models, or hybrid approaches for text-to-image synthesis.
Experiment with model architectures such as CNNs, RNNs, or attention-based models to determine the most suitable structure.
2. Data Collection and Annotation:
Curate and compile diverse datasets of text-image pairs for training the AI model.
Annotate the data, ensuring accurate alignments between textual descriptions and corresponding images.
3. Model Training and Optimization:
Implement training pipelines on powerful hardware, possibly utilizing GPU clusters or cloud-based infrastructure for accelerated model training.
Optimize hyperparameters, loss functions, and regularization techniques to improve model convergence and the quality of image synthesis.
4. Prototype Development:
Build a basic prototype to demonstrate the initial capabilities of the text-to-image AI system.
Verify and fine-tune the model’s performance with various text inputs to generate corresponding images.
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