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Fine-grained Angular Contrastive Learning with Coarse Labels
Few-shot learning methods offer pre-training techniques optimized for ea...
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StarNet: towards weakly supervised few-shot detection and explainable few-shot classification
In this paper, we propose a new few-shot learning method called StarNet,...
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MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification
Few-Shot Learning (FSL) is a topic of rapidly growing interest. Typicall...
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Baby steps towards few-shot learning with multiple semantics
Learning from one or few visual examples is one of the key capabilities ...
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Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level
Beauty is in the eye of the beholder. This maxim, emphasizing the subjec...
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NICE: Noise Injection and Clamping Estimation for Neural Network Quantization
Convolutional Neural Networks (CNN) are very popular in many fields incl...
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Delta-encoder: an effective sample synthesis method for few-shot object recognition
Learning to classify new categories based on just one or a few examples ...
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RepMet: Representative-based metric learning for classification and one-shot object detection
Distance metric learning (DML) has been successfully applied to object c...
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UNIQ: Uniform Noise Injection for non-uniform Quantization of neural networks
We present a novel method for training a neural network amenable to infe...
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UNIQ: Uniform Noise Injection for the Quantization of Neural Networks
We present a novel method for training deep neural network amenable to i...
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DeepISP: Learning End-to-End Image Processing Pipeline
We present DeepISP, a full end-to-end deep neural model of the camera im...
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