Piotr Dollár, Christian Wojek, Bernt Schiele, and Pietro Perona. There were different works on active learning for object detection based on hand-crafted features and shallow classifiers [1, 24]. The great success that deep models have achieved in the past is mainly o... Each prediction branch in the network is connected to a sigmoid function and the network is trained by minimizing: In this equation, λ is the regularization coefficient, pk(x)=σ(Θk(x)) is the posterior probability and X=(xi,yi) is the mini-batch of training samples where yi∈{0,1}KΘ is a binary vector. Neural Networks and Deep Learning is a free online book. is ∼150 ms, and ∼200 ms for a forward-backward pass. We have seen that specially relevant is the proposed max-pooling based aggregation step, which outperforms other proposals in the literature. Therefore, Step 3 computes an image-level scalar score for each image xu∈Xu by aggregating its pixel-level scores. 11/20/2019 ∙ by Hamed H. Aghdam, et al. On the one hand, setting b to 50 is impractical due to its high time-to-completion. Active learning for on-road vehicle detection: A comparative study. This taxonomy differentiates at the topmost level between the uncertainty of i.i.d. We use the set of images Xl, labeled with object bounding boxes, to train an object detector, Θ, based on a convolutional neural network. To this end, we divide the score matrix S into non-overlapping regions. This score has been previously used by [11] and [8] in the task of image classification. Given B, setting b is one of the important steps before performing active learning. Finally, an active learning cycle ends in Step 6 after retraining Θ using Xal and wl as initialization weights. To this end, we follow [3] and resize the logits spatially using the bilinear upsampling. For comparing our method with random sampling, we assume 14 cycles. where H is the entropy function. Neural networks are just one type of deep learning architecture. Earlier, we hypothesized that the divergence between predicted probability distributions in the neighborhood of false-positive and false-negative pixels should be high. An active learning method combining deep neural network and weighted sampling for structural reliability analysis 1. Setting b to 500 is more practical since the time-to-completion is ∼190 hours. In the latter case, temporal reasoning can be incorporated as a complementary selection. Acquiring such annotated data for semantic segmentation is a tedious and expensive task. Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, Song Han, William J. Therefore, Step 3 computes an image-level scalar score for each image xu∈Xu by aggregating its pixel-level scores. 0 Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. We have performed an ablative study of the different components of our method. Next, we explain the details of each step in our proposed method. More fundamentally, continual learning methods could offer enormous advantages for deep neural networks even in stationary settings, by improving learning efficiency as well as by enabling knowledge transfer between related tasks. Second, they mainly rely on simple merging functions such as average or maximum of pixel-level scores to obtain image-level scores. [2] Deep Bayesian Active Learning with Image Data, ICML, 2017 [3] Active Learning for Convolutional Neural Networks: A Core-Set Approach, ICLR, 2018 [4] Adversarial Active Learning for Deep Networks: a Margin Based Approach, arXiv, 2018

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