Christian11
Member
Use transfer learning, where a model trained on a large dataset is fine-tuned on your smaller dataset. Pre-trained models from Hugging Face, OpenAI, or TensorFlow Hub are great resources.
Another method is data augmentation, which synthetically increases your dataset via transformations (for images) or paraphrasing (for text). Synthetic data generation using GANs or simulation is also gaining traction. Lastly, consider few-shot or zero-shot learning using models like GPT-4 or LLaMA, which generalize well from minimal data.
SOURCE: https://www.inoru.com/ai-development-services
Another method is data augmentation, which synthetically increases your dataset via transformations (for images) or paraphrasing (for text). Synthetic data generation using GANs or simulation is also gaining traction. Lastly, consider few-shot or zero-shot learning using models like GPT-4 or LLaMA, which generalize well from minimal data.
SOURCE: https://www.inoru.com/ai-development-services