In responding to the above problem, this paper proposes a new algorithm CMLA ( C ontinual M eta- L earning A lgorithm) based on meta-learning. CMLA cannot only extract the key features of the sample, but also optimize the update method of the task gradient by introducing the cosine ...
CMLA not only reduces the instability of the adaptation process, but also solves the stability-plasticity dilemma to a certain extent, achieving the goal of continual learning. 展开 关键词: Continual meta-learning algorithm Deep learning Neural network Catastrophic forgetting Meta-learning ...
Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction [PDF2] [Copy] [Kimi14] Authors: Yichen Wu ; Long-Kai Huang ; Renzhen Wang ; Deyu Meng ; Yi…
Gradient-based learning, the most efficient and widely used paradigm, is an iterative algorithm that, at each iteration, makes a small change to the parameters in order to reduce the loss (for a more detailed explanation, see Box 2). The mechanics of this rule results in a tug-of-war ...
On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm Qi CHEN,Changjian Shui,Ligong Han,Mario Marchand FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning Dipam Goswami,Yuyang Liu,Bartłomiej Twardowski,Joost van de Weijer ...
This allowsOMLto take the effects of online continual learning such as catastrophic forgetting into account. Algorithm 1: Meta-Training : MAML-Rep Require: p(T ): di 44、stribution over CLP problems Require: , : step size hyperparameters Require: l: No of inner gradient steps 1:randomly ...
Artificial neural networks, deep-learning methods and the backpropagation algorithm1 form the foundation of modern machine learning and artificial intelligence. These methods are almost always used in two phases, one in which the weights of the network a
You could keep it simple and just retrain the same algorithm with the same parameters, but because we still want to gain really high accuracy we’re going to use AutoML. AutoML doesn’t have to be really complicated meta learning. You can just use hyperp...
it is important to utilize the knowledge in the current model to obtain efficient training and better performance. To address the above issues, in this paper, we propose GrowCLIP, a data-driven automatic model growing algorithm for contrastive language-image pre-training with continuous image-text...
(La-MAML), a fast optimisation-based meta-learning algorithm for online-continual learning, aided by a small episodic memory. Our proposed modulation of per-parameter learning rates in our meta-learning update allows us to draw connections to prior work on hypergradients and meta-descent. This ...