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https://arxiv.org/abs/1606.04080 Matching Networks for One Shot Learning Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new conce arxiv.org abstract

https://arxiv.org/abs/1905.01436 Edge-labeling Graph Neural Network for Few-shot Learning In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning. The previous graph neural network (GNN) approaches in few-shot learning have been based on th arxiv.org abstract

https://arxiv.org/abs/2003.06957v1 Frustratingly Simple Few-Shot Object Detection Detecting rare objects from a few examples is an emerging problem. Prior works show meta-learning is a promising approach. But, fine-tuning techniques have drawn scant attention. We find that fine-tuning only the last layer of existing detectors on rare cl arxiv.org abstract 적은 예시에서 희귀한 물체를 찾아내는것이 대두되고 있다. 선행 연구에 따..

https://arxiv.org/abs/1909.11862v2 Convolutional Neural Networks with Dynamic Regularization Regularization is commonly used for alleviating overfitting in machine learning. For convolutional neural networks (CNNs), regularization methods, such as DropBlock and Shake-Shake, have illustrated the improvement in the generalization performance. Howeve arxiv.org abstract 본 논문은 CNN을 위한 동적 정규화(Regulari..

https://arxiv.org/abs/2003.12047v1 Learning Inverse Rendering of Faces from Real-world Videos In this paper we examine the problem of inverse rendering of real face images. Existing methods decompose a face image into three components (albedo, normal, and illumination) by supervised training on synthetic face data. However, due to the domain gap be arxiv.org abstract 본 논문은 실제 얼굴 이미지의 역 렌더링 문제를 검..