Sentence examples for residual learning from inspiring English sources
Even though residual learning using ResNet-50 tends to benefit from lower number of parameters, they are found to be benefiial for classification instead of for gating.
Finally, a high-resolution image is achieved by the scheme of residual learning, which is implemented by superimposing the lost details and the original low-resolution image.
Inspired by other successful high-level works, Kim et al. [17] proposed to increase the network depth to have a larger receptive field to predict the image details and use the residual learning method to accelerate convergence.
During the testing phase, each of the ten successful subjects received the same five models ordered according to a balanced Latin square to minimize first-order residual learning effects during testing.
This spatial-temporal residual learning model is then utilized to connect the intra-frame and inter-frame redundancies within video sequences in a recurrent convolutional network and to predict HR temporal residues in the penultimate layer as guidance to benefit estimating the spatial residue for video SR.
Following immunization, and in the presence of a compromised striatal function, the PFC may allow residual learning abilities as observed within flexible DAT-i mice.
They spent the next several years at the New York State School for the Deaf in Rome, N.Y., where they developed an innovative curriculum that let even children with very little residual hearing learn, appreciate and express themselves through music.
EL-based interventions provide health professionals with an opportunity to interact with their patients in a more positive way, focusing on residual abilities and learning capacities rather than deficits and decline.
Kim et al. [17] proposed a very deep network to learn residual to accelerate the convergence speed.
In order to make better use of the image sparse prior, Zhang et al. [38] propose a dual dictionary to learn residual iteratively.
Table 4 Evaluation of offline learning reconstruction Dataset Residual distance (r) Disagreement r ( S ̈ ) r Δ ( S ̈ ) Δ CinC 2010(multi parameter) 1.00 0.03 0.14 0.05 MIT-BIH (two-channel) 1.01 0.40 0.1.01.02.
