Browse > Methodology > Transfer Learning

Transfer Learning

260 papers with code ยท Methodology

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.

State-of-the-art leaderboards

Latest papers without code

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

19 Aug 2019

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.

TRANSFER LEARNING

An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement Learning

19 Aug 2019

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.

TRANSFER LEARNING

Two-Staged Acoustic Modeling Adaption for Robust Speech Recognition by the Example of German Oral History Interviews

19 Aug 2019

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.

DATA AUGMENTATION ROBUST SPEECH RECOGNITION TRANSFER LEARNING

Transfer Learning-Based Label Proportions Method with Data of Uncertainty

19 Aug 2019

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.

TRANSFER LEARNING

Latent User Linking for Collaborative Cross Domain Recommendation

19 Aug 2019

With the widespread adoption of information systems, recommender systems are widely used for better user experience.

COLLABORATIVE FILTERING TRANSFER LEARNING

Transfer in Deep Reinforcement Learning using Knowledge Graphs

19 Aug 2019

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.

KNOWLEDGE GRAPHS QUESTION ANSWERING TRANSFER LEARNING

Language Graph Distillation for Low-Resource Machine Translation

17 Aug 2019

Neural machine translation on low-resource language is challenging due to the lack of bilingual sentence pairs.

MACHINE TRANSLATION TRANSFER LEARNING

Pseudo-task Regularization for ConvNet Transfer Learning

16 Aug 2019

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.

TRANSFER LEARNING

Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach

16 Aug 2019

Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems.

DIALOGUE GENERATION GOAL-ORIENTED DIALOGUE SYSTEMS TRANSFER LEARNING

Transferable Contrastive Network for Generalized Zero-Shot Learning

16 Aug 2019

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.

GENERALIZED ZERO-SHOT LEARNING TRANSFER LEARNING