Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with well-defined closed boundary can be dis
Layer Normalization (2016), J. Ba et al. Learning to learn by gradient descent by gradient descent (2016), M. Andrychowicz et al. Domain-adversarial training of neural networks (2016), Y. Ganin et al. WaveNet: A Generative Model for Raw Audio (2016)
Batch Normalization_ Accelerating Deep Network Training b.pdf Binarized Neural Networks_ Training Neural Networks with Weights and Activations Constrained to+ 1 or−1 Decoupled Neural Interfaces using Synthetic Gradients Dropout_ A Simple Way to Prev
we propose an intelligent cognitive radar system for detecting and classifying the micro unmanned aerial systems (micro UASs). In this system, we design a low- complexity binarized deep belief network (DBN) classifier that recognizes the signature p
AwesomeQRCode - An awesome(simple) QR code generator for Android. > 切换至中文（简体）版本？ Yay! Demo Available! Examples > Try to scan these QR codes below with your smart phone. Example 1 Example 2 Example 3 Solid dots instead of blocks Binarized With
Abstract. Tensor factorization has become an increasingly popular approach to knowledge graph completion (KGC), which is the task of automatically predicting missing facts in a knowledge graph. However, even with a simple model like CANDECOMP/PARAFA
深度学习中有关Model的论文集，包括《Batch Normalization accelerating deep network training》《Binarized Neural networks_training neural networks with weights and activations constrained ...》《Dropout_ a simple way to prevent neural networks》《improving neural networks
domingues-outlier-detection-evaluationdomingues-outlier-detection-evaluationNumerous machine learning methods are suitable for anomaly detection
However, supervised algorithms are more constraining than unsupervised meth-
ods as they need to be provi
Past research has demonstrated that digital Fresnel holograms can be binarized in a non-iterative manner by downsampling the source image with a grid lattice prior to the hologram generation process. The reconstructed image of a hologram that is bina
This paper studies visual pattern discovery in large-scale image collections via binarized mode seeking, where images can only be represented as binary codes for efficient storage and computation. We address this problem from the perspective of binar
