REVIEW 2 cited by
Feature Representation in Convolutional Neural Networks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Convolutional Neural Networks (CNNs) are powerful models that achieve impressive results for image classification. In addition, pre-trained CNNs are also useful for other computer vision tasks as generic feature extractors. This paper aims to gain insight into the feature aspect of CNN and demonstrate other uses of CNN features. Our results show that CNN feature maps can be used with Random Forests and SVM to yield classification results that outperforms the original CNN. A CNN that is less than optimal (e.g. not fully trained or overfitting) can also extract features for Random Forest/SVM that yield competitive classification accuracy. In contrast to the literature which uses the top-layer activations as feature representation of images for other tasks, using lower-layer features can yield better results for classification.
Forward citations
Cited by 2 Pith papers
-
Collaborative Prediction: To Join or To Disjoin Datasets
A data-driven rule decides when to merge or keep separate datasets for linear prediction, with a high-probability guarantee under Gaussian linear models.
-
Explaining Model Overfitting in CNNs via GMM Clustering
A CNN filter whose feature-map GMM clustering has small outlier clusters is called an anomaly filter and is claimed to indicate model overfitting, but the supporting experiments are weakly consistent.
Discussion (0). Continue with ORCID to comment.