Transfer learning is a methodology where weights from a model trained on one task are taken and either used (a) to construct a fixed feature extractor, (b) as weight initialization and/or fine-tuning.
( Image credit: Subodh Malgonde )
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AdaBoost-CNN is computationally efficient, as evidenced by the fact that the training simulation time of the proposed method is 47. 33 s, which is lower than the training simulation time required for a similar AdaBoost method without transfer learning, i. e. 225. 83 s on the imbalanced dataset.
Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions.
We present a novel resizing module for neural networks: shape adaptor, a drop-in enhancement built on top of traditional resizing layers, such as pooling, bilinear sampling, and strided convolution.
This also illustrates that the dataset can serve as a benchmark dataset to facilitate research on transfer learning algorithms.
Typically, better pre-trained models yield better transfer results, suggesting that initial accuracy is a key aspect of transfer learning performance.
In this paper, we propose an effective knowledge transfer framework to boost the weakly supervised object detection accuracy with the help of an external fully-annotated source dataset, whose categories may not overlap with the target domain.
To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis.
In our experiments, we show that through alteration along different dimensions, the model learns to encode distinct aspects of speech.
Reinforcement learning (RL) algorithms typically start tabula rasa, without any prior knowledge of the environment, and without any prior skills.