37,307 research outputs found
Unsupervised High-level Feature Learning by Ensemble Projection for Semi-supervised Image Classification and Image Clustering
This paper investigates the problem of image classification with limited or
no annotations, but abundant unlabeled data. The setting exists in many tasks
such as semi-supervised image classification, image clustering, and image
retrieval. Unlike previous methods, which develop or learn sophisticated
regularizers for classifiers, our method learns a new image representation by
exploiting the distribution patterns of all available data for the task at
hand. Particularly, a rich set of visual prototypes are sampled from all
available data, and are taken as surrogate classes to train discriminative
classifiers; images are projected via the classifiers; the projected values,
similarities to the prototypes, are stacked to build the new feature vector.
The training set is noisy. Hence, in the spirit of ensemble learning we create
a set of such training sets which are all diverse, leading to diverse
classifiers. The method is dubbed Ensemble Projection (EP). EP captures not
only the characteristics of individual images, but also the relationships among
images. It is conceptually simple and computationally efficient, yet effective
and flexible. Experiments on eight standard datasets show that: (1) EP
outperforms previous methods for semi-supervised image classification; (2) EP
produces promising results for self-taught image classification, where
unlabeled samples are a random collection of images rather than being from the
same distribution as the labeled ones; and (3) EP improves over the original
features for image clustering. The code of the method is available on the
project page.Comment: 22 pages, 8 figure
Semi-Supervised Learning via Compact Latent Space Clustering
We present a novel cost function for semi-supervised learning of neural
networks that encourages compact clustering of the latent space to facilitate
separation. The key idea is to dynamically create a graph over embeddings of
labeled and unlabeled samples of a training batch to capture underlying
structure in feature space, and use label propagation to estimate its high and
low density regions. We then devise a cost function based on Markov chains on
the graph that regularizes the latent space to form a single compact cluster
per class, while avoiding to disturb existing clusters during optimization. We
evaluate our approach on three benchmarks and compare to state-of-the art with
promising results. Our approach combines the benefits of graph-based
regularization with efficient, inductive inference, does not require
modifications to a network architecture, and can thus be easily applied to
existing networks to enable an effective use of unlabeled data.Comment: Presented as a long oral in ICML 2018. Post-conference camera read
Semi-supervised Skin Lesion Segmentation via Transformation Consistent Self-ensembling Model
Automatic skin lesion segmentation on dermoscopic images is an essential
component in computer-aided diagnosis of melanoma. Recently, many fully
supervised deep learning based methods have been proposed for automatic skin
lesion segmentation. However, these approaches require massive pixel-wise
annotation from experienced dermatologists, which is very costly and
time-consuming. In this paper, we present a novel semi-supervised method for
skin lesion segmentation by leveraging both labeled and unlabeled data. The
network is optimized by the weighted combination of a common supervised loss
for labeled inputs only and a regularization loss for both labeled and
unlabeled data. In this paper, we present a novel semi-supervised method for
skin lesion segmentation, where the network is optimized by the weighted
combination of a common supervised loss for labeled inputs only and a
regularization loss for both labeled and unlabeled data. Our method encourages
a consistent prediction for unlabeled images using the outputs of the
network-in-training under different regularizations, so that it can utilize the
unlabeled data. To utilize the unlabeled data, our method encourages the
consistent predictions of the network-in-training for the same input under
different regularizations. Aiming for the semi-supervised segmentation problem,
we enhance the effect of regularization for pixel-level predictions by
introducing a transformation, including rotation and flipping, consistent
scheme in our self-ensembling model. With only 300 labeled training samples,
our method sets a new record on the benchmark of the International Skin Imaging
Collaboration (ISIC) 2017 skin lesion segmentation challenge. Such a result
clearly surpasses fully-supervised state-of-the-arts that are trained with 2000
labeled data.Comment: BMVC 201
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images
Hyperspectral images (HSI) contain a wealth of information over hundreds of
contiguous spectral bands, making it possible to classify materials through
subtle spectral discrepancies. However, the classification of this rich
spectral information is accompanied by the challenges like high dimensionality,
singularity, limited training samples, lack of labeled data samples,
heteroscedasticity and nonlinearity. To address these challenges, we propose a
semi-supervised graph based dimensionality reduction method named
`semi-supervised spatial spectral regularized manifold local scaling cut'
(S3RMLSC). The underlying idea of the proposed method is to exploit the limited
labeled information from both the spectral and spatial domains along with the
abundant unlabeled samples to facilitate the classification task by retaining
the original distribution of the data. In S3RMLSC, a hierarchical guided filter
(HGF) is initially used to smoothen the pixels of the HSI data to preserve the
spatial pixel consistency. This step is followed by the construction of linear
patches from the nonlinear manifold by using the maximal linear patch (MLP)
criterion. Then the inter-patch and intra-patch dissimilarity matrices are
constructed in both spectral and spatial domains by regularized manifold local
scaling cut (RMLSC) and neighboring pixel manifold local scaling cut (NPMLSC)
respectively. Finally, we obtain the projection matrix by optimizing the
updated semi-supervised spatial-spectral between-patch and total-patch
dissimilarity. The effectiveness of the proposed DR algorithm is illustrated
with publicly available real-world HSI datasets
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Learning discrete representations of data is a central machine learning task
because of the compactness of the representations and ease of interpretation.
The task includes clustering and hash learning as special cases. Deep neural
networks are promising to be used because they can model the non-linearity of
data and scale to large datasets. However, their model complexity is huge, and
therefore, we need to carefully regularize the networks in order to learn
useful representations that exhibit intended invariance for applications of
interest. To this end, we propose a method called Information Maximizing
Self-Augmented Training (IMSAT). In IMSAT, we use data augmentation to impose
the invariance on discrete representations. More specifically, we encourage the
predicted representations of augmented data points to be close to those of the
original data points in an end-to-end fashion. At the same time, we maximize
the information-theoretic dependency between data and their predicted discrete
representations. Extensive experiments on benchmark datasets show that IMSAT
produces state-of-the-art results for both clustering and unsupervised hash
learning.Comment: To appear at ICML 201
Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labels
Graph Convolutional Networks(GCNs) play a crucial role in graph learning
tasks, however, learning graph embedding with few supervised signals is still a
difficult problem. In this paper, we propose a novel training algorithm for
Graph Convolutional Network, called Multi-Stage Self-Supervised(M3S) Training
Algorithm, combined with self-supervised learning approach, focusing on
improving the generalization performance of GCNs on graphs with few labeled
nodes. Firstly, a Multi-Stage Training Framework is provided as the basis of
M3S training method. Then we leverage DeepCluster technique, a popular form of
self-supervised learning, and design corresponding aligning mechanism on the
embedding space to refine the Multi-Stage Training Framework, resulting in M3S
Training Algorithm. Finally, extensive experimental results verify the superior
performance of our algorithm on graphs with few labeled nodes under different
label rates compared with other state-of-the-art approaches.Comment: AAAI Conference on Artificial Intelligence (AAAI 2020
Mumford-Shah Loss Functional for Image Segmentation with Deep Learning
Recent state-of-the-art image segmentation algorithms are mostly based on
deep neural networks, thanks to their high performance and fast computation
time. However, these methods are usually trained in a supervised manner, which
requires large number of high quality ground-truth segmentation masks. On the
other hand, classical image segmentation approaches such as level-set methods
are formulated in a self-supervised manner by minimizing energy functions such
as Mumford-Shah functional, so they are still useful to help generation of
segmentation masks without labels. Unfortunately, these algorithms are usually
computationally expensive and often have limitation in semantic segmentation.
In this paper, we propose a novel loss function based on Mumford-Shah
functional that can be used in deep-learning based image segmentation without
or with small labeled data. This loss function is based on the observation that
the softmax layer of deep neural networks has striking similarity to the
characteristic function in the Mumford-Shah functional. We show that the new
loss function enables semi-supervised and unsupervised segmentation. In
addition, our loss function can be also used as a regularized function to
enhance supervised semantic segmentation algorithms. Experimental results on
multiple datasets demonstrate the effectiveness of the proposed method.Comment: Accepted for IEEE Transactions on Image Processin
Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
We explore object discovery and detector adaptation based on unlabeled video
sequences captured from a mobile platform. We propose a fully automatic
approach for object mining from video which builds upon a generic object
tracking approach. By applying this method to three large video datasets from
autonomous driving and mobile robotics scenarios, we demonstrate its robustness
and generality. Based on the object mining results, we propose a novel approach
for unsupervised object discovery by appearance-based clustering. We show that
this approach successfully discovers interesting objects relevant to driving
scenarios. In addition, we perform self-supervised detector adaptation in order
to improve detection performance on the KITTI dataset for existing categories.
Our approach has direct relevance for enabling large-scale object learning for
autonomous driving.Comment: CVPR'18 submissio
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
Semi-supervised Classification: Cluster and Label Approach using Particle Swarm Optimization
Classification predicts classes of objects using the knowledge learned during
the training phase. This process requires learning from labeled samples.
However, the labeled samples usually limited. Annotation process is annoying,
tedious, expensive, and requires human experts. Meanwhile, unlabeled data is
available and almost free. Semi-supervised learning approaches make use of both
labeled and unlabeled data. This paper introduces cluster and label approach
using PSO for semi-supervised classification. PSO is competitive to traditional
clustering algorithms. A new local best PSO is presented to cluster the
unlabeled data. The available labeled data guides the learning process. The
experiments are conducted using four state-of-the-art datasets from different
domains. The results compared with Label Propagation a popular semi-supervised
classifier and two state-of-the-art supervised classification models, namely
k-nearest neighbors and decision trees. The experiments show the efficiency of
the proposed model
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