Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey

Image classification systems recently made a giant leap with the advancement of deep neural networks. However, these systems require an excessive amount of labeled data to be adequately trained. Gathering a correctly annotated dataset is not always feasible due to several factors, such as the expensiveness of the labeling process or difficulty of correctly classifying data, even for the experts. Because of these practical challenges, label noise is a common problem in real-world datasets, and numerous methods to train deep neural networks with label noise are proposed in the literature. Although deep neural networks are known to be relatively robust to label noise, their tendency to overfit data makes them vulnerable to memorizing even random noise. Therefore, it is crucial to consider the existence of label noise and develop counter algorithms to fade away its adverse effects to train deep neural networks efficiently. Even though an extensive survey of machine learning techniques under label noise exists, the literature lacks a comprehensive survey of methodologies centered explicitly around deep learning in the presence of noisy labels. This paper aims to present these algorithms while categorizing them into one of the two subgroups: noise model based and noise model free methods. Algorithms in the first group aim to estimate the noise structure and use this information to avoid the adverse effects of noisy labels. Differently, methods in the second group try to come up with inherently noise robust algorithms by using approaches like robust losses, regularizers or other learning paradigms

MetaLabelNet: Learning to Generate Soft-Labels from Noisy-Labels

Real-world datasets commonly have noisy labels, which negatively affects the performance of deep neural networks (DNNs). In order to address this problem, we propose a label noise robust learning algorithm, in which the base classifier is trained on soft-labels that are produced according to a meta-objective. In each iteration, before conventional training, the meta-objective reshapes the loss function by changing soft-labels, so that resulting gradient updates would lead to model parameters with minimum loss on meta-data. Soft-labels are generated from extracted features of data instances, and the mapping function is learned by a single layer perceptron (SLP) network, which is called MetaLabelNet. Following, base classifier is trained by using these generated soft-labels. These iterations are repeated for each batch of training data. Our algorithm uses a small amount of clean data as meta-data, which can be obtained effortlessly for many cases. We perform extensive experiments on benchmark datasets with both synthetic and real-world noises. Results show that our approach outperforms existing baselines

Meta Soft Label Generation for Noisy Labels

The existence of noisy labels in the dataset causes significant performance degradation for deep neural networks (DNNs). To address this problem, we propose a Meta Soft Label Generation algorithm called MSLG, which can jointly generate soft labels using meta-learning techniques and learn DNN parameters in an end-to-end fashion. Our approach adapts the meta-learning paradigm to estimate optimal label distribution by checking gradient directions on both noisy training data and noise-free meta-data. In order to iteratively update soft labels, meta-gradient descent step is performed on estimated labels, which would minimize the loss of noise-free meta samples. In each iteration, the base classifier is trained on estimated meta labels. MSLG is model-agnostic and can be added on top of any existing model at hand with ease. We performed extensive experiments on CIFAR10, Clothing1M and Food101N datasets. Results show that our approach outperforms other state-of-the-art methods by a large margin.Comment: Accepted by ICPR 202

A Comparative Study on Polygonal Mesh Simplification Algorithms

Polygonal meshes are a common way of representing three dimensional surface models in many different areas of computer graphics and geometry processing. However, with the evolution of the technology, polygonal models are becoming more and more complex. As the complexity of the models increase, the visual approximation to the real world objects get better but there is a trade-off between the cost of processing these models and better visual approximation. In order to reduce this cost, the number of polygons in a model can be reduced by mesh simplification algorithms. These algorithms are widely used such that nearly all of the popular mesh editing libraries include at least one of them. In this work, polygonal simplification algorithms that are embedded in open source libraries: CGAL, VTK and OpenMesh are compared with the Metro geometric error measuring tool. By this way we try to supply a guidance for developers for publicly available mesh libraries in order to implement polygonal mesh simplification

Elimination of Non-Novel Segments at Multi-Scale for Few-Shot Segmentation

Few-shot segmentation aims to devise a generalizing model that segments query images from unseen classes during training with the guidance of a few support images whose class tally with the class of the query. There exist two domain-specific problems mentioned in the previous works, namely spatial inconsistency and bias towards seen classes. Taking the former problem into account, our method compares the support feature map with the query feature map at multi scales to become scale-agnostic. As a solution to the latter problem, a supervised model, called as base learner, is trained on available classes to accurately identify pixels belonging to seen classes. Hence, subsequent meta learner has a chance to discard areas belonging to seen classes with the help of an ensemble learning model that coordinates meta learner with the base learner. We simultaneously address these two vital problems for the first time and achieve state-of-the-art performances on both PASCAL-5i and COCO-20i datasets.Comment: Accepted to WACV 202

Fpga Üzerinde Stereo Görüntü Alımı, Stereo Analizi Ve Derinlik Çıkarılması

Bu projede, kameralardan stereo görüntü alma, kamera kalibrasyonu ve stereo analizi ile derinlik çıkarma FPGA üzerinde gerçekleştirilecektir. Daha önce vermiş olduğumuz ve bu dönem tamamlamak üzere olduğumuz BAP projemizde (BAP-2008-03_01_04 - 3B Sahnelerde Nesnelerin Bölütlenmesi ve Kategorize Edilmesi) stereo kameralardan görüntü almak, kamera kalibrasyonu, stereo analizinin yapılması, derinlik çıkarmak gibi adımlar için algoritmalar belirlenmiş, geliştirilmiş ve yazılımsal olarak gerçeklenmiştir. Önerilen bu projede ise bu algoritmaların bir kısmının FPGA üzerinde donanımsal olarak gerçeklenmesi yapılacaktır

Effective connectivity modeling of human brain

Three different connectivity models, namely structural (anatomical links), functional (statistical dependencies), and effective (causal interactions) have been introduced to examine the interactions between different regions of the brain. To understand the interactive systems, it is of fundamental importance to distinguish the sender from the receiver, and hence to be able to estimate the direction of the information flow. Effective connectivity methods estimate direct and/or indirect causal relationships between the regions. Five different approaches have been proposed in the literature for functional and effective connectivity modeling of the brain: Covariance analysis, information theory, Granger causality based multivariate autoregressive modeling, dynamic causal modeling (DCM), dynamic Bayesian networks (DBN). These modeling approaches handle data in different domains such as time or frequency. All the methods use the data directly to establish a connectivity model with the exception of DCM that starts this process with a proposal model. Except DBN that is probabilistic, all the methods are deterministic. Most of the methods can produce linear models with only a few (DBN and information theory based methods) also being capable of constructing nonlinear models. Since DBN is the only probabilistic and nonlinear method that can model multivariate relations and it is data driven where no prior model suggestion or user tuned parameters are required for its structure learning, the results of DBN based effective connectivity modeling are presented for two studies: 1) Based on the theory that dyslexia is a disconnection syndrome, connectivity models of EEG data recorded from 31 dyslexic and 25 healthy children during word and pseudoword reading experiments are compared to determine whether there are differences between the two groups (dyslexic and control) and the two conditions (word and pseudoword). 2) To reveal the neural circuits for number perception in human brain and then to investigate the problems in these circuits in children with dyscalculia, connectivity models of fMRI data recorded from 6 healthy and 6 dyscalculic children during approximate and symbolic counting experiments are compared to determine whether there are differences between the two groups (dyscalculic and contro