The paper "Meta-Learning with Differentiable Convex Optimization" by Kwonjoon Lee proposes a novel approach to few-shot learning using linear classifiers as base learners. The authors argue that discriminatively trained linear predictors can offer better generalization compared to simple base learners like nearest-neighbor classifiers. They introduce MetaOptNet, which leverages the convex nature of linear classification problems to efficiently solve the meta-learning objective. Key contributions include the use of implicit differentiation of optimality conditions and the dual formulation of the optimization problem, allowing for high-dimensional feature embeddings with improved generalization. The approach achieves state-of-the-art performance on several few-shot learning benchmarks, including miniImageNet, tieredImageNet, CIFAR-FS, and FC100. The paper also discusses the impact of different base learners and embedding network architectures, showing that regularized linear models allow for higher embedding dimensions and reduced overfitting.The paper "Meta-Learning with Differentiable Convex Optimization" by Kwonjoon Lee proposes a novel approach to few-shot learning using linear classifiers as base learners. The authors argue that discriminatively trained linear predictors can offer better generalization compared to simple base learners like nearest-neighbor classifiers. They introduce MetaOptNet, which leverages the convex nature of linear classification problems to efficiently solve the meta-learning objective. Key contributions include the use of implicit differentiation of optimality conditions and the dual formulation of the optimization problem, allowing for high-dimensional feature embeddings with improved generalization. The approach achieves state-of-the-art performance on several few-shot learning benchmarks, including miniImageNet, tieredImageNet, CIFAR-FS, and FC100. The paper also discusses the impact of different base learners and embedding network architectures, showing that regularized linear models allow for higher embedding dimensions and reduced overfitting.