Technical Challenges in AI-Driven Visual Art Creation
Despite significant advancements, AI-generated art faces several technical challenges:
Computational Resource Demands: Training and running large models require substantial GPU resources and memory, which can be cost-prohibitive.
Bias in Training Data: Models trained on biased datasets may inadvertently reproduce or amplify these biases in generated content.
Artifact Generation: GANs and diffusion models sometimes produce visual artifacts, such as unnatural textures or distorted features, especially when handling complex compositions.
Interpretability: Understanding the decision-making process within deep learning models remains a challenge, making it difficult to predict or control specific aspects of the output.