10 skills found
oscarknagg / Few ShotRepository for few-shot learning machine learning projects
yaoyao-liu / Mini Imagenet ToolsTools for generating mini-ImageNet dataset and processing batches
yaoyao-liu / Few Shot Classification LeaderboardLeaderboards for few-shot image classification on miniImageNet, tieredImageNet, FC100, and CIFAR-FS.
gitabcworld / MatchingNetworksThis repo provides pytorch code which replicates the results of the Matching Networks for One Shot Learning paper on the Omniglot and MiniImageNet dataset
abdulfatir / Prototypical Networks TensorflowTensorflow implementation of NIPS 2017 Paper "Prototypical Networks for Few-shot Learning"
thuml / Few ShotA lightweight library that implements state-of-the-art few-shot learning algorithms.
HuHaigen / Adaptively Customizing Activation FunctionsTo enhance the nonlinearity of neural networks and increase their mapping abilities between the inputs and response variables, activation functions play a crucial role to model more complex relationships and patterns in the data. In this work, a novel methodology is proposed to adaptively customize activation functions only by adding very few parameters to the traditional activation functions such as Sigmoid, Tanh, and ReLU. To verify the effectiveness of the proposed methodology, some theoretical and experimental analysis on accelerating the convergence and improving the performance is presented, and a series of experiments are conducted based on various network models (such as AlexNet, VGGNet, GoogLeNet, ResNet and DenseNet), and various datasets (such as CIFAR10, CIFAR100, miniImageNet, PASCAL VOC and COCO) . To further verify the validity and suitability in various optimization strategies and usage scenarios, some comparison experiments are also implemented among different optimization strategies (such as SGD, Momentum, AdaGrad, AdaDelta and ADAM) and different recognition tasks like classification and detection. The results show that the proposed methodology is very simple but with significant performance in convergence speed, precision and generalization, and it can surpass other popular methods like ReLU and adaptive functions like Swish in almost all experiments in terms of overall performance.
EternalWang / CBMThis repository contains the code for the paper: Cooperative Bi-path Metric for Few-shot Learning, Zeyuan Wang, Yifan Zhao, Jia Li, Yonghong Tian, ACM Conference on Multimedia (ACM MM), 2020
mouniraziz / MsKPRNThe source code for Multi-Scale Kronecker-Product Relation Networks for Few-Shot Learning
XoriieInpottn / Maml PytorchMAML implementation in PyTorch.
