3,169 research outputs found
Neural Nearest Neighbors Networks
Non-local methods exploiting the self-similarity of natural signals have been
well studied, for example in image analysis and restoration. Existing
approaches, however, rely on k-nearest neighbors (KNN) matching in a fixed
feature space. The main hurdle in optimizing this feature space w.r.t.
application performance is the non-differentiability of the KNN selection rule.
To overcome this, we propose a continuous deterministic relaxation of KNN
selection that maintains differentiability w.r.t. pairwise distances, but
retains the original KNN as the limit of a temperature parameter approaching
zero. To exploit our relaxation, we propose the neural nearest neighbors block
(N3 block), a novel non-local processing layer that leverages the principle of
self-similarity and can be used as building block in modern neural network
architectures. We show its effectiveness for the set reasoning task of
correspondence classification as well as for image restoration, including image
denoising and single image super-resolution, where we outperform strong
convolutional neural network (CNN) baselines and recent non-local models that
rely on KNN selection in hand-chosen features spaces.Comment: to appear at NIPS*2018, code available at
https://github.com/visinf/n3net
Prototypical Contrastive Learning of Unsupervised Representations
This paper presents Prototypical Contrastive Learning (PCL), an unsupervised
representation learning method that addresses the fundamental limitations of
instance-wise contrastive learning. PCL not only learns low-level features for
the task of instance discrimination, but more importantly, it implicitly
encodes semantic structures of the data into the learned embedding space.
Specifically, we introduce prototypes as latent variables to help find the
maximum-likelihood estimation of the network parameters in an
Expectation-Maximization framework. We iteratively perform E-step as finding
the distribution of prototypes via clustering and M-step as optimizing the
network via contrastive learning. We propose ProtoNCE loss, a generalized
version of the InfoNCE loss for contrastive learning, which encourages
representations to be closer to their assigned prototypes. PCL outperforms
state-of-the-art instance-wise contrastive learning methods on multiple
benchmarks with substantial improvement in low-resource transfer learning. Code
and pretrained models are available at https://github.com/salesforce/PCL
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
In this paper, we study the problem of learning image classification models
with label noise. Existing approaches depending on human supervision are
generally not scalable as manually identifying correct or incorrect labels is
time-consuming, whereas approaches not relying on human supervision are
scalable but less effective. To reduce the amount of human supervision for
label noise cleaning, we introduce CleanNet, a joint neural embedding network,
which only requires a fraction of the classes being manually verified to
provide the knowledge of label noise that can be transferred to other classes.
We further integrate CleanNet and conventional convolutional neural network
classifier into one framework for image classification learning. We demonstrate
the effectiveness of the proposed algorithm on both of the label noise
detection task and the image classification on noisy data task on several
large-scale datasets. Experimental results show that CleanNet can reduce label
noise detection error rate on held-out classes where no human supervision
available by 41.5% compared to current weakly supervised methods. It also
achieves 47% of the performance gain of verifying all images with only 3.2%
images verified on an image classification task. Source code and dataset will
be available at kuanghuei.github.io/CleanNetProject.Comment: Accepted to CVPR 201
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