Debugging image classification algorithms using human assisted feedback

Becek, Kadircan
Neural Network debugging is a relatively newly emerged field of deep learning which tries to improve upon learned parameters with external assistance. Two academic fields are particularly active in addressing this issue: the field of feature visualiza- tions and explainable artificial intelligence. While these methods can improve how to interpret a neural network, they can not provide the expert opinion of a human to improve the accuracy of a neural network. Therefore, while being interpreted cor- rectly, these networks can have undesired outputs. For instance, they may have biases against some classes or may not work effectively in the wild due to overfitting. In this thesis, we propose a method with two steps to tackle this problem: (1) present- ing feature contribution using visualization, (2) taking feedback from humans, and retraining the network by disabling irrelevant or adversarial features.


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Semi-supervised learning is one of the dominantly utilized approaches to reduce the reliance of deep learning models on large-scale labeled data. One mostly used method of this approach is pseudo-labeling. However, pseudo-labeling, especially its originally proposed form tends to remarkably suffer from noisy training when the assigned labels are false. In order to mitigate this problem, in our work, we investigate the gradient sent to the neural network and propose a heuristic method, called competing label...
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Deep learning researchers and practitioners have accumulated a significant amount of experience on training a wide variety of architectures on various datasets. However, given anetwork architecture and a dataset, obtaining the best model (i.e. the model giving the smallest test set error) while keeping the training time complexity low is still a challenging task. Hyper-parameters of deep neural networks, especially the learning rate and its (decay) schedule, highly affect the network's final performance. Th...
Citation Formats
K. Becek, “Debugging image classification algorithms using human assisted feedback,” M.S. - Master of Science, Middle East Technical University, 2022.