In our research, we explore the boundaries of out-of-domain generalization by testing our model with images of imaginary objects, revealing its adaptability to unfamiliar contexts.
Through generating synthetic images of non-existent entities, we seek to evaluate the model's ability to generalize beyond the conventional datasets it was trained on, which is crucial for robust AI.
#out-of-domain-generalization #few-shot-learning #artificial-intelligence #synthetic-images #model-testing
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