A Concise Review of Transfer Learning

• URL: http://arxiv.org/abs/2104.02144v1
• Date: Mon, 5 Apr 2021 20:34:55 GMT
• Title: A Concise Review of Transfer Learning
• Authors: Abolfazl Farahani, Behrouz Pourshojae, Khaled Rasheed, Hamid R. Arabnia
• Abstract summary: Transfer learning aims to boost the performance of a target learner by applying another related source data. Traditional machine learning and data mining techniques assume that the training and testing data lie from the same feature space and distribution.
• Score: 1.5771347525430772
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The availability of abundant labeled data in recent years led the researchers to introduce a methodology called transfer learning, which utilizes existing data in situations where there are difficulties in collecting new annotated data. Transfer learning aims to boost the performance of a target learner by applying another related source data. In contrast to the traditional machine learning and data mining techniques, which assume that the training and testing data lie from the same feature space and distribution, transfer learning can handle situations where there is a discrepancy between domains and distributions. These characteristics give the model the potential to utilize the available related source data and extend the underlying knowledge to the target task achieving better performance. This survey paper aims to give a concise review of traditional and current transfer learning settings, existing challenges, and related approaches.

Related papers

• Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence [60.37934652213881]
  Domain Adaptation (DA) facilitates knowledge transfer from a source domain to a related target domain. This paper investigates a practical DA paradigm, namely Source data-Free Active Domain Adaptation (SFADA), where source data becomes inaccessible during adaptation. We present learn from the learnt (LFTL), a novel paradigm for SFADA to leverage the learnt knowledge from the source pretrained model and actively iterated models without extra overhead.
  arXiv  Detail & Related papers  (2024-07-26T17:51:58Z)
• An information-Theoretic Approach to Semi-supervised Transfer Learning [33.89602092349131]
  Transfer learning allows propagating information from one "source dataset" to another "target dataset" discrepancies between the underlying distributions of the source and target data are commonplace. We suggest novel information-theoretic approaches for the analysis of the performance of deep neural networks in the context of transfer learning.
  arXiv  Detail & Related papers  (2023-06-11T17:45:46Z)
• An Exploration of Data Efficiency in Intra-Dataset Task Transfer for Dialog Understanding [65.75873687351553]
  This study explores the effects of varying quantities of target task training data on sequential transfer learning in the dialog domain. Unintuitively, our data shows that often target task training data size has minimal effect on how sequential transfer learning performs compared to the same model without transfer learning.
  arXiv  Detail & Related papers  (2022-10-21T04:36:46Z)
• A Data-Based Perspective on Transfer Learning [76.30206800557411]
  We take a closer look at the role of the source dataset's composition in transfer learning. Our framework gives rise to new capabilities such as pinpointing transfer learning brittleness.
  arXiv  Detail & Related papers  (2022-07-12T17:58:28Z)
• Probing transfer learning with a model of synthetic correlated datasets [11.53207294639557]
  Transfer learning can significantly improve the sample efficiency of neural networks. We re-think a solvable model of synthetic data as a framework for modeling correlation between data-sets. We show that our model can capture a range of salient features of transfer learning with real data.
  arXiv  Detail & Related papers  (2021-06-09T22:15:41Z)
• What is being transferred in transfer learning? [51.6991244438545]
  We show that when training from pre-trained weights, the model stays in the same basin in the loss landscape. We present that when training from pre-trained weights, the model stays in the same basin in the loss landscape and different instances of such model are similar in feature space and close in parameter space.
  arXiv  Detail & Related papers  (2020-08-26T17:23:40Z)
• On the application of transfer learning in prognostics and health management [0.0]
  Data availability has encouraged researchers and industry practitioners to rely on data-based machine learning. Deep learning, models for fault diagnostics and prognostics more than ever. These models provide unique advantages, however, their performance is heavily dependent on the training data and how well that data represents the test data. transfer learning is an approach that can remedy this issue by keeping portions of what is learned from previous training and transferring them to the new application.
  arXiv  Detail & Related papers  (2020-07-03T23:35:18Z)
• Uniform Priors for Data-Efficient Transfer [65.086680950871]
  We show that features that are most transferable have high uniformity in the embedding space. We evaluate the regularization on its ability to facilitate adaptation to unseen tasks and data.
  arXiv  Detail & Related papers  (2020-06-30T04:39:36Z)
• Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks [27.44348371795822]
  We develop a statistical minimax framework to characterize the limits of transfer learning. We derive a lower-bound for the target generalization error achievable by any algorithm as a function of the number of labeled source and target data.
  arXiv  Detail & Related papers  (2020-06-16T22:49:26Z)
• Unsupervised Transfer Learning with Self-Supervised Remedy [60.315835711438936]
  Generalising deep networks to novel domains without manual labels is challenging to deep learning. Pre-learned knowledge does not transfer well without making strong assumptions about the learned and the novel domains. In this work, we aim to learn a discriminative latent space of the unlabelled target data in a novel domain by knowledge transfer from labelled related domains.
  arXiv  Detail & Related papers  (2020-06-08T16:42:17Z)
• A survey on domain adaptation theory: learning bounds and theoretical guarantees [17.71634393160982]
  The main objective of this survey is to provide an overview of the state-of-the-art theoretical results in a specific, and arguably the most popular, sub-field of transfer learning. In this sub-field, the data distribution is assumed to change across the training and the test data, while the learning task remains the same. We provide a first up-to-date description of existing results related to domain adaptation problem.
  arXiv  Detail & Related papers  (2020-04-24T16:11:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.