143,752 research outputs found
Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach
The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to speed-up the detection of landmarks in bioimages. We extensively evaluate our method variants on three different datasets (cephalometric, zebrafish, and drosophila images). We identify the key method parameters (notably the multi-resolution) and report results with respect to human ground truths and existing methods. Our method achieves recognition performances competitive with current existing approaches while being generic and fast. The algorithms are integrated in the open-source Cytomine software and we provide parameter configuration guidelines so that they can be easily exploited by end-users. Finally, datasets are readily available through a Cytomine server to foster future research
Curriculum Dropout
Dropout is a very effective way of regularizing neural networks.
Stochastically "dropping out" units with a certain probability discourages
over-specific co-adaptations of feature detectors, preventing overfitting and
improving network generalization. Besides, Dropout can be interpreted as an
approximate model aggregation technique, where an exponential number of smaller
networks are averaged in order to get a more powerful ensemble. In this paper,
we show that using a fixed dropout probability during training is a suboptimal
choice. We thus propose a time scheduling for the probability of retaining
neurons in the network. This induces an adaptive regularization scheme that
smoothly increases the difficulty of the optimization problem. This idea of
"starting easy" and adaptively increasing the difficulty of the learning
problem has its roots in curriculum learning and allows one to train better
models. Indeed, we prove that our optimization strategy implements a very
general curriculum scheme, by gradually adding noise to both the input and
intermediate feature representations within the network architecture.
Experiments on seven image classification datasets and different network
architectures show that our method, named Curriculum Dropout, frequently yields
to better generalization and, at worst, performs just as well as the standard
Dropout method.Comment: Accepted at ICCV (International Conference on Computer Vision) 201
Learning Transferable Architectures for Scalable Image Recognition
Developing neural network image classification models often requires
significant architecture engineering. In this paper, we study a method to learn
the model architectures directly on the dataset of interest. As this approach
is expensive when the dataset is large, we propose to search for an
architectural building block on a small dataset and then transfer the block to
a larger dataset. The key contribution of this work is the design of a new
search space (the "NASNet search space") which enables transferability. In our
experiments, we search for the best convolutional layer (or "cell") on the
CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking
together more copies of this cell, each with their own parameters to design a
convolutional architecture, named "NASNet architecture". We also introduce a
new regularization technique called ScheduledDropPath that significantly
improves generalization in the NASNet models. On CIFAR-10 itself, NASNet
achieves 2.4% error rate, which is state-of-the-art. On ImageNet, NASNet
achieves, among the published works, state-of-the-art accuracy of 82.7% top-1
and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than
the best human-invented architectures while having 9 billion fewer FLOPS - a
reduction of 28% in computational demand from the previous state-of-the-art
model. When evaluated at different levels of computational cost, accuracies of
NASNets exceed those of the state-of-the-art human-designed models. For
instance, a small version of NASNet also achieves 74% top-1 accuracy, which is
3.1% better than equivalently-sized, state-of-the-art models for mobile
platforms. Finally, the learned features by NASNet used with the Faster-RCNN
framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO
dataset
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