Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks(2017,MAML)提出了MAML模型,也是目前最经典的meta-learning算法之一,是很多后续meta-learning研究的基础,理解MAML算法原理是理解meta-learning的核心。首先我们明确MAML的目的,传统的机器学习方法是在样本维度进行学习,在单个任务的训练集上训练模型,希望模...
Copy the params for inner loopfast_weights=OrderedDict(model.named_parameters())### --- INNER TRAIN LOOP --- ###forinner_stepinrange(inner_train_step):# Simply trainingtrain_label=create_label(n_way,k_shot).to(device)logits=model.functional_forward(support_set,fast_weights)loss=criterion(...
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks(2017,MAML)提出了MAML模型,也是目前最经典的meta-learning算法之一,是很多后续meta-learning研究的基础,理解MAML算法原理是理解meta-learning的核心。首先我们明确MAML的目的,传统的机器学习方法是在样本维度进行学习,在单个任务的训练集上训练模型,希望模...
[1] Ravi, Sachin and Larochelle, Hugo. Optimization as a model for few-shot learning. In International Conference on Learning Representations (ICLR), 2017. 这篇文章的核心思想是下面这一段: 怎么把LSTM的更新和梯度下降联系起来才是更值得思考的问题吧。 2.5 面向RL的Meta Learning方法 基本思路:既然Met...
Further functions are invoked to generate feature vector set and machine learning model(s) are trained using the features vector set. Implementation of the generated data meta-model enables re-using of feature engineering code.Mishra MayankKunde Shruti...
Stacking is a type of ensemble learning algorithm. Ensemble learning refers to machine learning algorithms that combine the predictions for two or more predictive models. Stacking uses another machine learning model, a meta-model, to learn how to best combine the predictions of the contributing ...
表一MAML算法伪代码,出自论文Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks 步骤一:随机初始化参数; 步骤二:开始循环; 步骤三:导入图片,随即对几个task进行采样,形成一个batch; 步骤四---七:声明一下,这里每次迭代涉及到两次参数更新,由于个人习惯,笔者将第一次称之为外循环,第二次称之为...
and_print ("Your role is a machine learning expert who gives highly technical advice to senior engineers who work with complicated datasets. Explain the pros and cons of using PyTorch.")# Often results in more technical benefits and drawbacks that provide more technical details on how model ...
在model pre-training中,用正在训练的模型来计算loss,而maml中使用训练好的模型 下图中横轴是model parameter,如果是maml中,我们不在意现在的表现,比如图中的phi做初始的参数,将顺着gradient的方向,在两个任务上都走的很好 在model pre-training中,在意现在的表现,但不保证训练后会好。
Flamingo: a Visual Language Model for Few-Shot Learning (2022) 本文介绍了 Flamingo，这是一种专为 VLP 任务中的小样本学习而设计的视觉语言模型 (VLM)。虽然以前的研究主要关注语言或视觉的小样本学习，但 Flamingo 专门解决了组合 VLP 领域的挑战。Flamingo 利用预先训练的模型进行图像理解和语言生成，从而减少...