ArticlePattern recognition2018
On Automated Source Selection for Transfer Learning in Convolutional Neural Networks.
Article in Pattern recognition, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRI.Pattern recognition · 2025Article
- Pre-training, personalization, and self-calibration: all a neural network-based myoelectric decoder needs.Frontiers in neurorobotics · 2025Article
- Functional extreme learning machine.Frontiers in computational neuroscience · 2023Article
- Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review.Journal of imaging · 2021Review
- Deep Transfer Learning for Time Series Data Based on Sensor Modality Classification.Sensors (Basel, Switzerland) · 2020Article
- Polyp detection during colonoscopy using a regression-based convolutional neural network with a tracker.Pattern recognition · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Transfer learning, or inductive transfer, refers to the transfer of knowledge from a source task to a target task. In the context of convolutional neural networks (CNNs), transfer learning can be implemented by transplanting the learned feature layers from one CNN (derived from the source task) to initialize another (for the target task). Previous research has shown that the choice of the source CNN impacts the performance of the target task. In the current literature, there is no principled way for selecting a source CNN for a given target task despite the increasing availability of pre-trained source CNNs. In this paper we investigate the possibility of automatically ranking source CNNs prior to utilizing them for a target task. In particular, we present an information theoretic framework to understand the source-target relationship and use this as a basis to derive an approach to automatically rank source CNNs in an efficient, zero-shot manner. The practical utility of the approach is thoroughly evaluated using the PlacesMIT dataset, MNIST dataset and a real-world MRI database. Experimental results demonstrate the efficacy of the proposed ranking method for transfer learning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.