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https://arxiv.org/abs/2003.07845v1 Rethinking Batch Normalization in Transformers The standard normalization method for neural network (NN) models used in Natural Language Processing (NLP) is layer normalization (LN). This is different than batch normalization (BN), which is widely-adopted in Computer Vision. The preferred use of LN in arxiv.org abstract NLP에서 사용되는 Neural network 모델의 표준 정규화 방법은 ..

https://arxiv.org/abs/2003.05891v1 SASL: Saliency-Adaptive Sparsity Learning for Neural Network Acceleration Accelerating the inference speed of CNNs is critical to their deployment in real-world applications. Among all the pruning approaches, those implementing a sparsity learning framework have shown to be effective as they learn and prune the models in an end- arxiv.org abstract 본 논문은 CNN의 추론..

https://arxiv.org/abs/2002.08791v1 Bayesian Deep Learning and a Probabilistic Perspective of Generalization The key distinguishing property of a Bayesian approach is marginalization, rather than using a single setting of weights. Bayesian marginalization can particularly improve the accuracy and calibration of modern deep neural networks, which are typically und arxiv.org Abstract Bayesin 접근법의 핵..

https://arxiv.org/abs/1911.09996v3 Orderless Recurrent Models for Multi-label Classification Recurrent neural networks (RNN) are popular for many computer vision tasks, including multi-label classification. Since RNNs produce sequential outputs, labels need to be ordered for the multi-label classification task. Current approaches sort labels accor arxiv.org abstract 본 논문은 예측된 레이블 순서에 따른 ground t..

https://arxiv.org/abs/1901.03465v3 Hand Segmentation and Fingertip Tracking from Depth Camera Images Using Deep Convolutional Neural Network and Multi-task SegNet Hand segmentation and fingertip detection play an indispensable role in hand gesture-based human-machine interaction systems. In this study, we propose a method to discriminate hand components and to locate fingertips in RGB-D images. ..