resnet

Wang, Min, Baoyuan Liu, and Hassan Foroosh. "Factorized convolutional neural Networks." ArXiv preprint (2016). This paper focuses on the optimization of the convolution layer in the deep network, which has three unique features:-Can be trained directly . You do not need to train the original model first, then use the sparse, compressed bits and so on to compress.-Maintain the original input and output of the convolution layer, it is easy to replace the already designed network.- simple to implem

using the same as the equivalent of the energy of PCA, the current layer of γ all add up, and then in order from large to small, select the larger part, usually choose about 70% (Specific situation analysis). The effect of lambda selection on Γ is as shown in the figure:Lambda is 0, the target function does not punish γ, Lambda equals 1e-5, you can find that the γ=0.0+ has more than 450, the whole is to 0 near. When Λ=1e-4, there was a greater sparse constraint on γ, and it was possible to see

combined with an additional self-organizing layer to form a complete network. We use the term CNN microarchitecture to refer to the specific organization and size of each module. 2.3 CNN Macro Structure Although the CNN micro architecture involves a single layer and module, we define the CNN macro architecture as a system-level organization of multiple modules to the End-to-end CNN architecture. Perhaps the most widely studied CNN macro-Architecture topic in recent literature is the impact of d

, knowledge distillation can not be grouped into any category. And the author of Structured simplification is divided into three categories:Tensor factorization:mobilenet as the representative, the authors suggest that this method cannot decompose 1*1 convolution, while 1*1 convolution is commonly used in googlenet, ResNet and Xception sparse Connection: Deep compression as the representative, this method disadvantage is that the pruning of the conne

Congratulations, you've become a member of the AI Engineer Group. You can then collect some of your own data and train your own identification engine, or try to optimize the model, feel the pain of the so-called resnet, or simply try to inception these more advanced networks to brush CIFAR , or you can try to learn from NLP or strengthen your learning direction. In short, these things are far less difficult than they seem. Of course, no matter the ro

artificial intelligence. A typical example is resnet-50[5], which has 50-tier convolution networks, more than 95MB of storage requirements, and the floating-point multiplication times needed to compute each picture. If you prune some of the redundant weights, it will probably save 75% of the parameters and 50% of the calculation time. It is important to use these methods to compress models for devices such as handsets and FPGA, which have only megaby

hierarchical Question-image co-attention for Visual Question answering Jiasen lu, jianwei yang, dhruv batra, Devi Parikh (Submitted on to (V1), last revised Jan (this version, V5)) A number of recent works has proposed attention models for Visual Question answering (VQA) that Generate spatial maps highlighting image regions relevant to answering the question. In this paper, we argue the in addition to modeling ' where to look ' or visual attention, it's equally important to model "What words-l

constraints are very limited, so the resulting results are more serious to ignore, as shown below, and Gan can avoid this problem. Although Psnr and Ssim are smaller (suggesting that the images we have recovered from Gan are not particularly accurate (as opposed to the actual image, the details of the supplement may be different from the actual details, such as the pattern on the head of the figure in the image below, and the collar on the neck, with a distinct texture structure, And the actual

recognition. ArXiv preprint arxiv:1409.1556 (vggnet) [PDF] szegedy, Christian, et al going deeper with convolutions. Proceedings of the IEEE Conference on computer Vision and Pattern recognition. 2015. (googlenet) [PDF] Szegedy C, Vanhoucke V, Ioffe S, et al rethinking the Inception Architecture for computer Vis ION[J]. Computer Science, 2015:2818-2826. (INCEPTION-V3) [PDF] He, Kaiming, et al. Deep residual learning for image recognition. ArXiv preprint arxiv:1512.03385. (

has alexnet[5], vgg-net[6], GOOGLENET[7], Inception v2-v4[8, 9], resnet[10] and so on. 2.2 An effective network training technique-fine tuning (fine-tune) We do not need to start from scratch a parameter to the experiment to construct a deep network, because there are already a lot of published papers have helped us do these verification, we just need to stand on the shoulders of predecessors, to choose a suitable network structure just fine. And cho