# On the Transferability of Representations in Neural Networks Between Datasets and Tasks

arXiv: https://arxiv.org/abs/1811.12273 · HTML: https://mlsanity.com/paper/1811.12273/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes](https://mlsanity.com/paper/2107.11954/) (2021)
- [Rethinking Two Consensuses of the Transferability in Deep Learning](https://mlsanity.com/paper/2212.00399/) (2022)
- [CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback](https://mlsanity.com/paper/2210.09505/) (2022)
- [Quantum Transfer Learning Shows Improved Robustness in Low-Data Regimes](https://mlsanity.com/paper/2605.09118/) (2026)

## Abstract (arXiv)

Deep networks, composed of multiple layers of hierarchical distributed representations, tend to learn low-level features in initial layers and transition to high-level features towards final layers. Paradigms such as transfer learning, multi-task learning, and continual learning leverage this notion of generic hierarchical distributed representations to share knowledge across datasets and tasks. Herein, we study the layer-wise transferability of representations in deep networks across a few datasets and tasks and note some interesting empirical observations.