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Found 7 papers, 3 papers with code
AMF: Adaptable Weighting Fusion with Multiple Fine-tuning for Image Classification
Fine-tuning is widely applied in image classification tasks as a transfer learning approach.
Deep transfer learning for image classification: a survey
We show that under this new taxonomy, many of the applications where transfer learning has been shown to be ineffective or even hinder performance are to be expected when taking into account the source and target datasets and the techniques used.
Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologies
Convolutional neural networks (CNNs) have demonstrated rapid progress and a high level of success in object detection.
Feature Selection on Thermal-stress Dataset
These findings are likely to improve the accuracy of current stress recognition systems.
Exploring Biases and Prejudice of Facial Synthesis via Semantic Latent Space
Varying the proportions of male and female faces in the training data can have a substantial effect on behavior on the test data: we found that the seemingly obvious choice of 50:50 proportions was not the best for this dataset to reduce biased behavior on female faces, which was 71% unbiased as compared to our top unbiased rate of 84%.
Non-binary deep transfer learning for image classification
A model is either pre-trained or not pre-trained.
Ranked #2 on
Image Classification
on Caltech-256
(using extra training data)
Pairwise-GAN: Pose-based View Synthesis through Pair-Wise Training
This inspired our research which explores the performance of two models from pixel transformation in frontal facial synthesis, Pix2Pix and CycleGAN.
