… channel measurements and we exploit their learned prior to provide higher channel estimation
… Our results show that transfer learning techniques are effective in accessing the learned …

… Modern convolutional neural networks trained on large scale … feature reuse plays a
very important role in transfer learning, … of the data, utility of untrained representations, pre-training …

… In Transfer Learning, an untrained model benefits from a previously trained model and its
dataset… We thought that model would start gaining the ability to distinguish even the smallest bit …

… transfer learning architecture consists of two parts; the first part is constructed with a pretrained
deep neural network that serves to extract the features … , an untrained neural network B, …

… Neural Network (CNN) layers have improved deep learning … An untrained DL uses a random
initializing weight for nodes, … feature reuse plays a very important role in transfer learning, …

… extractor that maps an input into a fixed dimensional feature … feature extractor, transferring
knowledge from an already … rapidly with the number of untrained layers, reaching more than 20…

… features instead of graph structures, which we deem more essential in graph transfer
learning … (through comparison against the untrained GIN encoders). The recently proposed …

… that features within a network are general, we will be able to use them for transfer learning
(… We also compare to random, untrained weights because Jarrett et al. (2009) showed — …

… Compared to random, untrained weights, PINN achieves an average performance … transfer
learning approach to neural network-based surrogate models to investigate the transfer ability …

… transfer learning, this study delves into the manual initialization of untrained neural networks
… process of visual learning and “mirroring” seen in humans; the ability to mimic something …