Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss,程序员大本营,技术文章内容聚合第一站。
A typical edge detection algorithm called Prewitt was used for tumor segmentation task, based on the output of the tumor localization. Overall performance of the proposed tumor segmentation architecture, was analyzed using objective quality parameters including Accuracy, Boundary Displacement Error (BDE),...
Adaptive Loss-aware Quantization for Multi-bit Networks. paper * 本文是一篇混合精度的 Multi-bit Network 量化文章。文中提出了一种Adaptive Loss-aware Quantization(ALQ)的模块,通过直接优化量化所带来的error来实现无损bit化,同时还引入了pruning的操作,来裁剪部分不重要的权重。ALQ无需梯度近似,同时也能够量化...
19. The system of claim 18, wherein the first of the plurality of vector instructions is associated with a first task and the first of one or more other instructions is associated with a second task. 20. The system of claim 1, wherein the at least one respective operand comprises at le...
Also, we work on quantizing spectral-spatial deep neural networks, and on validating their performance over the patch-based splits of hyperspectral benchmark sets [62]. Finally, varying the number of quantization levels could shed more light on the abilities of deep models deployed over different...
In reality, existing quantization techniques fail to replicate their success on lightweight architectures such as MobileNet. To this end, we present a novel fully differentiable non-uniform quantizer that can be seamlessly mapped onto efficient ternary-based dot product engines. We conduct comprehensive...
Cisse et al. [131] 提出了针对于task losses的对抗样本,可以针对语音样本生成对抗样本,也可以迁移到人体姿势识别应用上。 ATN Baluja and Fischer [42] 训练了一种生成对抗样本的前向网络,名为对抗转化网络(ATN),主要有两部分的loss,一部分loss使得原图和对抗图尽可能相似,另外一部分则使得对抗样本识别错误。
Word sense disambiguation techniques, powered by deep learning, help in identifying the correct meaning of a word based on its context. Semantic role labeling, another important task, involves identifying the semantic relationships between predicates and their arguments in a sentence. Deep learning ...
the visual features of an image into words. Through translation, we're generating a new representation of that image, rather than just generating new meaning. Viewing it as translation, and only by extension generation, scopes the task in a different light, and makes it a bit more intuitive....
2017-NIPS-Federated Multi-Task Learning 2017-NIPS-Towards Accurate Binary Convolutional Neural Network 2017-NIPS-Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations 2017-NIPS-TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning 2017-NIPS-Flexpoint...