Authors - Nevil Dhinoja, Shubh Patel, Binal Kaka Abstract - Gradient conflicts, computational complexity, and optimization instability are some of the issues with model-agnostic meta-learning, or MAML. We introduce a methodical methodology that integrates three improvement techniques: meta-level regularization, adaptive optimization management, and taskaware gradient. By combining three complimentary mechanisms—task-aware gradient modulation, meta-level regularization, and adaptive optimization management—this work suggests an organized design framework to increase the stability and robustness of MAML-based optimization. The paradigm provides a solid basis for the methodical creation of more reliable and scalable meta-learning systems, even while empirical evaluation is saved for later research.
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