Due to the enclosed chip evacuation space in deep hole drilling process, chips are accumulated in drill flutes as drilling depth increases, resulting in the increase of drilling torque and lead to drill breakage. Peck drilling is a widely used method to periodically alleviate the drilling torque ...
, this is then fed into their CNN to produce an initial segmentation prediction, the user can then provide scribbles to mark areas of the image as mis-classified - these user inputs are then weighted heavily in the calculation of the refined segmentation using their graph cut based algorithm....
We present a novel active learning algorithm, termed as iterative surrogate model optimization (ISMO), for robust and efficient numerical approximation of PDE constrained optimization problems. This algorithm is based on deep neural networks and its key feature is the iterative selection of training dat...
Regarding the partially auto-regressive architecture of our model, the application of the commonly used beam search algorithm to generate the top-k predictions is not feasible. Therefore, we have designed an inference module specifically tailored for our model as shown in Fig.1b. Given that the ...
In recent years, there has been a shift towards deep learning-based approaches in the research on low-light image enhancement. LLNet25is a pioneering work by LLIE that performs contrast enhancement and denoising based on a depth autoencoder. However, the relationship between real-world illumination...
3D reconstruction and self-localisation in a single optimization framework. Recently deep learning based frameworks [23,24] have been used to improve classical feature based SLAM algorithms [19]; the idea is either to provide single view depth estimation or directly computing frame-to-frame local ...
First, the SFTMD is pre-trained using mean square error (MSE) loss. We then train the predictor network and the corrector network alternately. The parameters of the trained SFTMD are fixed during training the predictor and the cor- rector. The order of training can refer to ...
The flow of our LNS-Net is given in Algorithm 1. We separately optimize each clustering task T i and train a net- work that contains three proposed modules to implement the functions e(·), c(·) and ψ(·) respectively. Once T i has been optimized, we s...
Finally, as a computational imaging method, the iterative algorithm for high-resolution 3D reconstruction involves high computational costs, which may be substantially accelerated by deep learning techniques. The potential migrasome functions in immune response and tumor metastasis are speculations based on ...
Recently deep learning based frameworks [23,24] have been used to improve classical fea- ture based SLAM algorithms [19]; the idea is either to provide single view depth estimation or directly computing frame-to-frame local feature correspondences. Our proposed method shares some common traits ...