# A Concise Review of Transfer Learning

arXiv: https://arxiv.org/abs/2104.02144 · HTML: https://mlsanity.com/paper/2104.02144/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [A Brief Review of Domain Adaptation](https://mlsanity.com/paper/2010.03978/) (2020)
- [Correlation Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1612.01939/) (2016)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [Efficient Learning of Domain-invariant Image Representations](https://mlsanity.com/paper/1301.3224/) (2013)
- [DeepMSRF: A novel Deep Multimodal Speaker Recognition framework with Feature selection](https://mlsanity.com/paper/2007.06809/) (2020)
- [On Parameter Tuning in Meta-learning for Computer Vision](https://mlsanity.com/paper/2003.00837/) (2020)
- [Video Contents Understanding using Deep Neural Networks](https://mlsanity.com/paper/2004.13959/) (2020)

## Built on by (most cited first, 13 of 13)

- [The Impact of Geometric Complexity on Neural Collapse in Transfer Learning](https://mlsanity.com/paper/2405.15706/) (2024)
- [Computer Vision in the Food Industry: Accurate, Real-time, and Automatic Food Recognition with Pretrained MobileNetV2](https://mlsanity.com/paper/2405.11621/) (2024)
- [Hybrid BYOL-ViT: Efficient approach to deal with small datasets](https://mlsanity.com/paper/2111.04845/) (2021)
- [Leveraging Large Language Models and Topic Modeling for Toxicity Classification](https://mlsanity.com/paper/2411.17876/) (2024)
- [Target inductive methods for zero-shot regression](https://mlsanity.com/paper/2402.01252/) (2024)
- [Extracting and Combining Abilities For Building Multi-lingual Ability-enhanced Large Language Models](https://mlsanity.com/paper/2410.07825/) (2024)
- [Borch: A Deep Universal Probabilistic Programming Language](https://mlsanity.com/paper/2209.06168/) (2022)
- [A Transformer Framework for Data Fusion and Multi-Task Learning in Smart Cities](https://mlsanity.com/paper/2211.10506/) (2022)
- [Decoding the Alphabet Soup of Degrees in the United States Postsecondary Education System Through Hybrid Method: Database and Text Mining](https://mlsanity.com/paper/2309.13050/) (2023)
- [BanglaNLP at BLP-2023 Task 2: Benchmarking different Transformer Models for Sentiment Analysis of Bangla Social Media Posts](https://mlsanity.com/paper/2310.09238/) (2023)
- [Direct side information learning for zero-shot regression](https://mlsanity.com/paper/2402.01264/) (2024)
- [A Novel Method for Accurate & Real-time Food Classification: The Synergistic Integration of EfficientNetB7, CBAM, Transfer Learning, and Data Augmentation](https://mlsanity.com/paper/2410.02304/) (2024)
- [Trustworthy deep domain adaptation for wearable photoplethysmography signal analysis with decision-theoretic uncertainty quantification](https://mlsanity.com/paper/2604.17480/) (2026)

## Abstract (arXiv)

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.