Meta-learning is a methodology considered with "learning to learn" machine learning algorithms.
( Image credit: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks )
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Recent progress has demonstrated that such meta-learning methods may exceed scalable human-invented architectures on image classification tasks.
#3 best model for Semantic Segmentation on PASCAL VOC 2012 test (using extra training data)
Specifically, we target semi-supervised classification performance, and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations useful for this task.
If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes.
#18 best model for Few-Shot Image Classification on OMNIGLOT - 1-Shot, 20-way
To address the issue, we propose a novel transfer learning approach based on meta-learning that can automatically learn what knowledge to transfer from the source network to where in the target network.
The move from hand-designed features to learned features in machine learning has been wildly successful.
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning.
#2 best model for Few-Shot Image Classification on OMNIGLOT - 5-Shot, 5-way
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.