Generalizing from a few examples: A survey on few-shot learning
Machine learning has been highly successful in data-intensive applications but is often
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …
hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to …
Deep learning for retail product recognition: Challenges and techniques
Taking time to identify expected products and waiting for the checkout in a retail store are
common scenes we all encounter in our daily lives. The realization of automatic product …
common scenes we all encounter in our daily lives. The realization of automatic product …
Deepemd: Few-shot image classification with differentiable earth mover's distance and structured classifiers
In this paper, we address the few-shot classification task from a new perspective of optimal
matching between image regions. We adopt the Earth Mover's Distance (EMD) as a metric to …
matching between image regions. We adopt the Earth Mover's Distance (EMD) as a metric to …
Meta-transfer learning for few-shot learning
Meta-learning has been proposed as a framework to address the challenging few-shot
learning setting. The key idea is to leverage a large number of similar few-shot tasks in order …
learning setting. The key idea is to leverage a large number of similar few-shot tasks in order …
Deepemd: Differentiable earth mover's distance for few-shot learning
In this work, we develop methods for few-shot image classification from a new perspective of
optimal matching between image regions. We employ the Earth Mover's Distance (EMD) as …
optimal matching between image regions. We employ the Earth Mover's Distance (EMD) as …
Meta-transfer learning through hard tasks
Meta-learning has been proposed as a framework to address the challenging few-shot
learning setting. The key idea is to leverage a large number of similar few-shot tasks in order …
learning setting. The key idea is to leverage a large number of similar few-shot tasks in order …
Fusing multi-scale context-aware information representation for automatic in-field pest detection and recognition
Automatic in-field pest detection and recognition using mobile vision technique is a hot topic
in modern intelligent agriculture, but suffers from serious challenges including complexity of …
in modern intelligent agriculture, but suffers from serious challenges including complexity of …
Deepfake Catcher: Can a Simple Fusion be Effective and Outperform Complex DNNs?
Despite having completely different configurations deep learning architectures learn a
specific set of features that are common across architectures. For example the initial few …
specific set of features that are common across architectures. For example the initial few …
PiTLiD: identification of plant disease from leaf images based on convolutional neural network
K Liu, X Zhang - IEEE/ACM Transactions on Computational …, 2022 - ieeexplore.ieee.org
With the development of plant phenomics, the identification of plant diseases from leaf
images has become an effective and economic approach in plant disease science. Among …
images has become an effective and economic approach in plant disease science. Among …
On-device indoor positioning: A federated reinforcement learning approach with heterogeneous devices
The widespread deployment of machine learning techniques in ubiquitous computing
environments has sparked interests in exploiting the vast amount of data stored on mobile …
environments has sparked interests in exploiting the vast amount of data stored on mobile …