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ClassCut for Unsupervised Class Segmentation [PDF]
, 2010 We propose a novel method for unsupervised class segmentation on a set of images. It alternates between segmenting object instances and learning a class model.
Bogdan Alexe +5 more
core +1 more source
Applied Sciences, 2022 Supervised image denoising methods based on deep neural networks require a large amount of noisy-clean or noisy image pairs for network training. Thus, their performance drops drastically when the given noisy image is significantly different from the ...
Shaoping Xu +5 more
doaj +1 more source
Breast Image Classification Based on Multi-feature Joint Supervised Dictionary Learning [PDF]
Jisuanji gongcheng, 2018 Aiming at the problem that the unsupervised dictionary learning algorithm has low image classification accuracy,a supervised dictionary learning classification algorithm which combines with multiple image features is proposed.It uses the convolution ...
LIU Lihui,XU Jun,GONG Lei
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Supervising Unsupervised Learning
CoRR, 2017 We introduce a framework to leverage knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets. Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithms.
Vikas K. Garg 0001, Adam Kalai
openaire +2 more sources
Memristor Neural Network Training with Clock Synchronous Neuromorphic System
Micromachines, 2019 Memristor devices are considered to have the potential to implement unsupervised learning, especially spike timing-dependent plasticity (STDP), in the field of neuromorphic hardware research.
Sumin Jo +5 more
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Unsupervised learning in neuromemristive systems [PDF]
2015 National Aerospace and Electronics Conference (NAECON), 2015 Neuromemristive systems (NMSs) currently represent the most promising platform to achieve energy efficient neuro-inspired computation. However, since the research field is less than a decade old, there are still countless algorithms and design paradigms to be explored within these systems.
Cory E. Merkel, Dhireesha Kudithipudi
openaire +3 more sources
Unsupervised online multitask learning of behavioral sentence embeddings [PDF]
PeerJ Computer Science, 2019 Appropriate embedding transformation of sentences can aid in downstream tasks such as NLP and emotion and behavior analysis. Such efforts evolved from word vectors which were trained in an unsupervised manner using large-scale corpora.
Shao-Yen Tseng +2 more
doaj +2 more sources
On The Philosophy of Unsupervised Learning
SSRN Electronic Journal, 2022 AbstractUnsupervised learning algorithms are widely used for many important statistical tasks with numerous applications in science and industry. Yet despite their prevalence, they have attracted remarkably little philosophical scrutiny to date. This stands in stark contrast to supervised and reinforcement learning algorithms, which have been widely ...
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TUMK-ELM: A Fast Unsupervised Heterogeneous Data Learning Approach
IEEE Access, 2018 Advanced unsupervised learning techniques are an emerging challenge in the big data era due to the increasing requirements of extracting knowledge from a large amount of unlabeled heterogeneous data.
Lingyun Xiang +4 more
doaj +1 more source
IEEE Access, 2022 Unsupervised learning of feature representations is a challenging yet important problem for analyzing a large collection of multimedia data that do not have semantic labels.
Takahiko Furuya, Ryutarou Ohbuchi
doaj +1 more source