Our implementation provides flexibility of tracking global and/or per-episode running statistics, hence supporting both transductive and inductive inference.īetter data pre-processing. Implementation Highlightsīatch normalization with per-episode running statistics. We highlight the improvements we have built into our code, and discuss our observations that warrent some attention. Other existing PyTorch implementations typically see a ~3% gap in accuracy for the 5-way-1-shot and 5-way-5-shot classification tasks on mini-ImageNet.īeyond reproducing the results, our implementation comes with a few extra bits that we believe can be helpful for further development of the framework. To the best of our knowledge, this is the only PyTorch implementation of MAML to date that fully reproduces the results in the original paper without applying tricks such as data augmentation, evaluation on multiple crops, and ensemble of multiple models. This repository contains code for training and evaluating MAML on the mini-ImageNet and tiered-ImageNet datasets most commonly used for few-shot image classification. We faithfully reproduce the official Tensorflow implementation while incorporating a number of additional features that may ease further study of this very high-profile meta-learning framework. Interactions with neighbors can help infuse their models with new information and speed up adaptation to new tasks.MAML in PyTorch - Re-implementation and BeyondĪ PyTorch implementation of Model Agnostic Meta-Learning (MAML). This setting is very natural to consider for meta-learning, where different agents can be assumed to have local meta-learners based on their own experiences. However, there does not appear to exist works that consider model agnostic meta-learning in a decentralized multi-agent setting. Furthermore, some works used MAML for signal processing applications such as image segmentation, speech recognition, and demodulation. ![]() Several works have extended and/or analyzed this approach to great effect such as –. ![]() The work proposed a model-agnostic meta-learning (MAML) approach, which is an initial parameter-transfer methodology where the goal is to learn a good “ launch model”. This evident gap has motivated a growing number of works on learning architectures that learn to learn (see for a recent survey). Human intelligence, on the other hand, is characterized by a remarkable ability to leverage prior knowledge to accelerate adaptation to new tasks. However, the generalization guarantees apply only to test data following the same distribution as the training data. Training of highly expressive learning architectures, such as deep neural networks, requires large amounts of data in order to ensure high generalization performance. Simulation results illustrate the theoretical findings and the superior performance relative to the traditional non-cooperative setting. The work provides a detailed theoretical analysis to show that the proposed strategy allows a collection of agents to attain agreement at a linear rate and to converge to a stationary point of the aggregate MAML objective even in non-convex environments. Decentralized optimization algorithms are superior to centralized implementations in terms of scalability, robustness, avoidance of communication bottlenecks, and privacy guarantees. ![]() Motivated by this observation, we propose a cooperative fully-decentralized multi-agent meta-learning algorithm, referred to as Diffusion-based MAML or Dif-MAML. The formalism of meta-learning is actually well-suited for this decentralized setting, where the learner benefits from information and computational power spread across the agents. It is more natural to encounter situations where these resources are spread across several agents connected by some graph topology. Given the amount of resources that are needed, it is generally difficult to expect the tasks, their respective data, and the necessary computational capacity to be available at a single central location. Meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount of data per task. The objective of meta-learning is to exploit knowledge obtained from observed tasks to improve adaptation to unseen tasks.
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