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Found 6 papers, 1 papers with code
Image Similarity using An Ensemble of Context-Sensitive Models
In this work, we present a novel approach for building an image similarity model based on labelled data in the form of A:R vs B:R. We address the challenges of sparse sampling in the image space (R, A, B) and biases in the models trained with context-based data by using an ensemble model.
Background Invariance Testing According to Semantic Proximity
This ontology enables (i) efficient and meaningful search for background scenes of different semantic distances to a target image, (ii) quantitative control of the distribution and sparsity of the sampled background scenes, and (iii) quality assurance using visual representations of invariance testing results (referred to as variance matrices).
ML4ML: Automated Invariance Testing for Machine Learning Models
In this paper, we show that testing the invariance qualities of ML models may result in complex visual patterns that cannot be classified using simple formulas.
Simultaneous Adversarial Training - Learn from Others Mistakes
Adversarial examples are maliciously tweaked images that can easily fool machine learning techniques, such as neural networks, but they are normally not visually distinguishable for human beings.
Transfer Learning for Action Unit Recognition
This paper presents a classifier ensemble for Facial Expression Recognition (FER) based on models derived from transfer learning.
Local Deep Neural Networks for Age and Gender Classification
We tested the proposed modified local deep neural networks approach on the LFW and Adience databases for the task of gender and age classification.
