Add to ORKGThe generalization power of the pre-trained model is the key for few-shot deep learning. Dropout is a regularization technique used in traditional deep learning methods. In this paper, we explore the power of dropout on few-shot learning and provide some insights about how to use it. Extensive experiments on the few-shot object detection and few-shot image classification datasets, i.e., Pascal VOC, MS COCO, CUB, and mini-ImageNet, validate the effectiveness of our method.Comment: arXiv admin note: substantial text overlap with arXiv:2210.0640
Few-shot image generation aims to train generative models using a small number of training images. W...
Few-shot classification aims to adapt to new tasks with limited labeled examples. To fully use the a...
Single image-level annotations only correctly describe an often small subset of an image's content, ...
Few-shot classification requires deep neural networks to learn generalized representations only from...
In many machine learning tasks, the available training data has a skewed distribution- a small set o...
Deep learning has achieved enormous success in various computer tasks. The excellent performance dep...
Few-shot learning focuses on learning a new visual concept with very limited labelled examples. A su...
Most CNN models rely on the large-scale annotated training data, and the performance turns to be lo...
Humans are able to learn to recognize new objects even from a few examples. In contrast, training de...
Business analytics and machine learning have become essential success factors for various industries...
Deep learning has recently driven remarkable progress in several applications, including image class...
Deep Learning approaches have recently raised the bar in many fields, from Natural Language Processi...
CVPR 2019Training deep neural networks from few examples is a highly challenging and key problem for...
Few-shot image classification aims to accurately classify unlabeled images using only a few labeled ...
In this work, we propose to use out-of-distribution samples, i.e., unlabeled samples coming from out...
Few-shot image generation aims to train generative models using a small number of training images. W...
Few-shot classification aims to adapt to new tasks with limited labeled examples. To fully use the a...
Single image-level annotations only correctly describe an often small subset of an image's content, ...
Few-shot classification requires deep neural networks to learn generalized representations only from...
In many machine learning tasks, the available training data has a skewed distribution- a small set o...
Deep learning has achieved enormous success in various computer tasks. The excellent performance dep...
Few-shot learning focuses on learning a new visual concept with very limited labelled examples. A su...
Most CNN models rely on the large-scale annotated training data, and the performance turns to be lo...
Humans are able to learn to recognize new objects even from a few examples. In contrast, training de...
Business analytics and machine learning have become essential success factors for various industries...
Deep learning has recently driven remarkable progress in several applications, including image class...
Deep Learning approaches have recently raised the bar in many fields, from Natural Language Processi...
CVPR 2019Training deep neural networks from few examples is a highly challenging and key problem for...
Few-shot image classification aims to accurately classify unlabeled images using only a few labeled ...
In this work, we propose to use out-of-distribution samples, i.e., unlabeled samples coming from out...
Few-shot image generation aims to train generative models using a small number of training images. W...
Few-shot classification aims to adapt to new tasks with limited labeled examples. To fully use the a...
Single image-level annotations only correctly describe an often small subset of an image's content, ...