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.
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More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.
In this paper, we propose a self-adaptive fuzzy reinforcement learning-based performance (stress) testing framework (SaFReL) that enables the tester agent to learn the optimal policy for generating stress test cases leading to performance breaking point without access to performance model of the system under test.
In automatic speech recognition, often little training data is available for specific challenging tasks, but training of state-of-the-art automatic speech recognition systems requires large amounts of annotated speech.
Learning with label proportions (LLP), which is a learning task that only provides unlabeled data in bags and each bag's label proportion, has widespread successful applications in practice.
With the widespread adoption of information systems, recommender systems are widely used for better user experience.
Text adventure games, in which players must make sense of the world through text descriptions and declare actions through text descriptions, provide a stepping stone toward grounding action in language.
Neural machine translation on low-resource language is challenging due to the lack of bilingual sentence pairs.
The contributions of this paper are: a) PtR provides an effective and efficient alternative for regularization without dependence on concrete tasks or extra data; b) desired strength of regularization effect in PtR is dynamically adjusted and maintained based on the gradient norms of the target objective and the pseudo-task.
Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems.
Zero-shot learning (ZSL) is a challenging problem that aims to recognize the target categories without seen data, where semantic information is leveraged to transfer knowledge from some source classes.