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02/12/2020
Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space
Recent advances in the field of artificial intelligence have been made p...
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09/06/2019
Coarse2Fine: A Two-stage Training Method for Fine-grained Visual Classification
Small inter-class and large intra-class variations are the main challeng...
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05/27/2019
Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Few-shot learning is an important area of research. Conceptually, humans...
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10/18/2018
Gradient Agreement as an Optimization Objective for Meta-Learning
This paper presents a novel optimization method for maximizing generaliz...
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09/21/2018
A Meta-Learning Approach for Custom Model Training
Transfer-learning and meta-learning are two effective methods to apply k...
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04/09/2015
Unsupervised Feature Learning from Temporal Data
Current state-of-the-art classification and detection algorithms rely on...
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12/18/2014
Unsupervised Learning of Spatiotemporally Coherent Metrics
Current state-of-the-art classification and detection algorithms rely on...
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11/19/2014
End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression
Deformable Parts Models and Convolutional Networks each have achieved no...
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11/18/2014
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture
In this paper we address three different computer vision tasks using a s...
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06/09/2014
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
Predicting depth is an essential component in understanding the 3D geome...
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12/21/2013
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
We present an integrated framework for using Convolutional Networks for ...
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12/16/2013
Learning Factored Representations in a Deep Mixture of Experts
Mixtures of Experts combine the outputs of several "expert" networks, ea...
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12/06/2013