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1. Evaluation of Gender Bias in Facial Recognition with Traditional Machine Learning Algorithms

   https://doi.org/10.1109/SSCI50451.2021.9660186  

   Atay, Mustafa; Gipson, Hailey; Gwyn, Tony; Roy, Kaushik (December 2021, IEEE Symposium Series on Computational Intelligence (SSCI))

   The prevalent commercial deployment of automated facial analysis systems such as face recognition as a robust authentication method has increasingly fueled scientific attention. Current machine learning algorithms allow for a relatively reliable detection, recognition, and categorization of face images comprised of age, race, and gender. Algorithms with such biased data are bound to produce skewed results. It leads to a significant decrease in the performance of state-of-the-art models when applied to images of gender or ethnicity groups. In this paper, we study the gender bias in facial recognition with gender balanced and imbalanced training sets using five traditional machine learning algorithms. We aim to report the machine learning classifiers which are inclined towards gender bias and the ones which mitigate it. Miss rates metric is effective in finding out potential bias in predictions. Our study utilizes miss rates metric along with a standard metric such as accuracy, precision or recall to evaluate possible gender bias effectively. 

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2. An automated approach for improving the inference latency and energy efficiency of pretrained CNNs by removing irrelevant pixels with focused convolutions

   https://doi.org/10.1109/ASP-DAC58780.2024.10473884  

   Tung, Caleb; Eliopoulos, Nicholas; Jajal, Purvish; Ramshankar, Gowri; Yang, Cheng-Yun; Synovic, Nicholas; Zhang, Xuecen; Chaudhary, Vipin; Thiruvathukal, George K; Lu, Yung-Hsiang (January 2024, Asia and South Pacific Design Automation Conference (ASP-DAC))

   Computer vision often uses highly accurate Convolutional Neural Networks (CNNs), but these deep learning models are associated with ever-increasing energy and computation requirements. Producing more energy-efficient CNNs often requires model training which can be cost-prohibitive. We propose a novel, automated method to make a pretrained CNN more energyefficient without re-training. Given a pretrained CNN, we insert a threshold layer that filters activations from the preceding layers to identify regions of the image that are irrelevant, i.e. can be ignored by the following layers while maintaining accuracy. Our modified focused convolution operation saves inference latency (by up to 25%) and energy costs (by up to 22%) on various popular pretrained CNNs, with little to no loss in accuracy 

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3. Demographic bias mitigation at test-time using uncertainty estimation and human–machine partnership

   Krishnan, Anoop; Rattani, Ajita (December 2024, Machine learning with applications)

   NA (Ed.)

   Facial attribute classification algorithms frequently manifest demographic biases by obtaining differential performance across gender and racial groups. Existing bias mitigation techniques are mostly in-processing techniques, i.e., implemented during the classifier’s training stage, that often lack generalizability, require demographically annotated training sets, and exhibit a trade-off between fairness and classification accuracy. In this paper, we propose a technique to mitigate bias at the test time i.e., during the deployment stage, by harnessing prediction uncertainty and human–machine partnership. To this front, we propose to utilize those lowest percentages of test data samples identified as outliers with high prediction uncertainty. These identified uncertain samples at test-time are labeled by human analysts for decision rendering and for subsequently retraining the deep neural network in a continual learning framework. With minimal human involvement and through iterative refinement of the network with human guidance at test-time, we seek to enhance the accuracy as well as the fairness of the already deployed facial attribute classification algorithms. Extensive experiments are conducted on gender and smile attribute classification tasks using four publicly available datasets and with gender and race as the protected attributes. The obtained outcomes consistently demonstrate improved accuracy by up to 2% and 5% for the gender and smile attribute classification tasks, respectively, using our proposed approaches. Further, the demographic bias was significantly reduced, outperforming the State-of-the-Art (SOTA) bias mitigation and baseline techniques by up to 55% for both classification tasks. 

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4. Deep Learning Detection and Recognition of Spot Elevations on Historical Topographic Maps

   https://doi.org/10.3389/fenvs.2022.804155  

   Arundel, Samantha T.; Morgan, Trenton P.; Thiem, Phillip T. (February 2022, Frontiers in Environmental Science)

   Some information contained in historical topographic maps has yet to be captured digitally, which limits the ability to automatically query such data. For example, U.S. Geological Survey’s historical topographic map collection (HTMC) displays millions of spot elevations at locations that were carefully chosen to best represent the terrain at the time. Although research has attempted to reproduce these data points, it has proven inadequate to automatically detect and recognize spot elevations in the HTMC. We propose a deep learning workflow pretrained using large benchmark text datasets. To these datasets we add manually crafted training image/label pairs, and test how many are required to improve prediction accuracy. We find that the initial model, pretrained solely with benchmark data, fails to predict any HTMC spot elevations correctly, whereas the addition of just 50 custom image/label pairs increases the predictive ability by ∼50%, and the inclusion of 350 data pairs increased performance by ∼80%. Data augmentation in the form of rotation, scaling, and translation (offset) expanded the size and diversity of the training dataset and vastly improved recognition accuracy up to ∼95%. Visualization methods, such as heat map generation and salient feature detection, can be used to better understand why some predictions fail. 

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5. ScaleNet - Improve CNNs through Recursively Rescaling Objects

   https://doi.org/10.1609/aaai.v34i07.6806  

   Li, Xingyi; Qi, Zhongang; Fern, Xiaoli; Li, Fuxin (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   Deep networks are often not scale-invariant hence their performance can vary wildly if recognizable objects are at an unseen scale occurring only at testing time. In this paper, we propose ScaleNet, which recursively predicts object scale in a deep learning framework. With an explicit objective to predict the scale of objects in images, ScaleNet enables pretrained deep learning models to identify objects in the scales that are not present in their training sets. By recursively calling ScaleNet, one can generalize to very large scale changes unseen in the training set. To demonstrate the robustness of our proposed framework, we conduct experiments with pretrained as well as fine-tuned classification and detection frameworks on MNIST, CIFAR-10, and MS COCO datasets and results reveal that our proposed framework significantly boosts the performances of deep networks. 

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