55,128 research outputs found
Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networks
Deep clustering has recently attracted significant attention. Despite the
remarkable progress, most of the previous deep clustering works still suffer
from two limitations. First, many of them focus on some distribution-based
clustering loss, lacking the ability to exploit sample-wise (or
augmentation-wise) relationships via contrastive learning. Second, they often
neglect the indirect sample-wise structure information, overlooking the rich
possibilities of multi-scale neighborhood structure learning. In view of this,
this paper presents a new deep clustering approach termed Image clustering with
contrastive learning and multi-scale Graph Convolutional Networks (IcicleGCN),
which bridges the gap between convolutional neural network (CNN) and graph
convolutional network (GCN) as well as the gap between contrastive learning and
multi-scale neighborhood structure learning for the image clustering task. The
proposed IcicleGCN framework consists of four main modules, namely, the
CNN-based backbone, the Instance Similarity Module (ISM), the Joint Cluster
Structure Learning and Instance reconstruction Module (JC-SLIM), and the
Multi-scale GCN module (M-GCN). Specifically, with two random augmentations
performed on each image, the backbone network with two weight-sharing views is
utilized to learn the representations for the augmented samples, which are then
fed to ISM and JC-SLIM for instance-level and cluster-level contrastive
learning, respectively. Further, to enforce multi-scale neighborhood structure
learning, two streams of GCNs and an auto-encoder are simultaneously trained
via (i) the layer-wise interaction with representation fusion and (ii) the
joint self-adaptive learning that ensures their last-layer output distributions
to be consistent. Experiments on multiple image datasets demonstrate the
superior clustering performance of IcicleGCN over the state-of-the-art
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
Learning-based approaches to robotic manipulation are limited by the
scalability of data collection and accessibility of labels. In this paper, we
present a multi-task domain adaptation framework for instance grasping in
cluttered scenes by utilizing simulated robot experiments. Our neural network
takes monocular RGB images and the instance segmentation mask of a specified
target object as inputs, and predicts the probability of successfully grasping
the specified object for each candidate motor command. The proposed transfer
learning framework trains a model for instance grasping in simulation and uses
a domain-adversarial loss to transfer the trained model to real robots using
indiscriminate grasping data, which is available both in simulation and the
real world. We evaluate our model in real-world robot experiments, comparing it
with alternative model architectures as well as an indiscriminate grasping
baseline.Comment: ICRA 201
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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