The phrase "previous learnings" is correct and usable in written English.
You can use it to refer to knowledge or skills acquired in the past. For example: "I will draw upon my previous learnings to inform my decision".
The previous deep-learning-based image SR approaches always learn an end-to-end mapping, which takes the low-resolution image as input and directly outputs the high-resolution one.
The neurons of these previous deep-learning-based methods work on the whole input feature map.
However, the previous deep-learning-based image super-resolution methods rarely take image prior into account.
Previous machine learning-based approaches to identifying TFBSs commonly map a known TFBS to a one-dimensional vector using its physicochemical properties.
On the other hand, in all the previous deep-learning-based methods, each neuron works on the whole input feature map.
Compared to the previous deep-learning-based methods, the neurons in our proposed SR network pay more attention to acquire the local image information to avoid compromise to different image contents.
Compared to previous deep-learning-based single-image SR methods, our proposed method is also an end-to-end mapping that takes the low-resolution image as input and directly outputs the high-resolution one.
Further variables of interest or potential confounders, which were also modelled as fixed effects were students' gender, age, previous knowledge, e-learning interest and e-learning experience.
This included a justification being based not only on best evidence, but also local data (jurisdictional/national), previous experiences and learnings and local expert opinion.
In view of the deficiencies in previous research, a learning-based synchronous (LS) approach from forwarding nodes is proposed to reduce the delay for IIoTs while not reducing the network lifetime.
Traditional hand engineered features lack in generalizing well to other domains/Problems, are time consuming, expensive, requires expert knowledge on the problem domain and doesn't facilitate learning from previous learnings/Representations(Transfer learning).
