Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels

By Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael F. P. O'Boyle, Amos J. Storkey. 2020

163 citations recorded by the source corpus.

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Citations in the corpus

Kernel Continual Learning (2021, background): “Recently, they have also been demonstrated to be strong learners in tandem with deep neural networks (Wilson et al., 2016a;b; Tossou et al., 2019), especially when learning from limited data (Zhen et al., 2020; Patacchiola et al., 2020).”
On Episodes, Prototypical Networks, and Few-shot Learning (2020, methodology): “Finally, two recent meta-learning approaches based on Gaussian Processes [33, 46] also”
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning (2020, unknown): “, [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], target at effectively adapting model parameters to new tasks in the low-shot regime.”
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities (2022, methodology): “Table 10 compares representative meta-learning methods, where the MAML-based approaches [133, 146] further optimize the inner and outer loop to achieve new results on few-shot classiication tasks, and the approaches based on new metrics [186, 188] have been greatly improved compared to the approache”
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes (2020, methodology): “Deep Kernel Transfer (DKT) (Patacchiola et al., 2020) uses Gaussian processes with least squares classification to perform few-shot classification and learns covariance functions parameterized by deep neural networks.”