A Comprehensive Literature Review on Convolutional Neural Networks

The fields of computer vision and image processing from their initial days have been dealing with the problems of visual recognition. Convolutional Neural Networks (CNNs) in machine learning are deep architectures built as feed-forward neural networks or perceptrons, which are inspired by the research done in the fields of visual analysis by the visual cortex of mammals like cats. This work gives a detailed analysis of CNNs for the computer vision tasks, natural language processing, fundamental sciences and engineering problems along with other miscellaneous tasks. The general CNN structure along with its mathematical intuition and working, a brief critical commentary on the advantages and disadvantages, which leads researchers to search for alternatives to CNN’s are also mentioned. The paper also serves as an appreciation of the brain-child of past researchers for the existence of such a fecund architecture for handling multidimensional data and approaches to improve their performance further

Machine Learning Advances for Practical Problems in Computer Vision

Convolutional neural networks (CNN) have become the de facto standard for computer vision tasks, due to their unparalleled performance and versatility. Although deep learning removes the need for extensive hand engineered features for every task, real world applications of CNNs still often require considerable engineering effort to produce usable results. In this thesis, we explore solutions to problems that arise in practical applications of CNNs. We address a rarely acknowledged weakness of CNN object detectors: the tendency to emit many excess detection boxes per object, which must be pruned by non maximum suppression (NMS). This practice relies on the assumption that highly overlapping boxes are excess, which is problematic when objects are occluding overlapping detections are actually required. Therefore we propose a novel loss function that incentivises a CNN to emit exactly one detection per object, making NMS unnecessary. Another common problem when deploying a CNN in the real world is domain shift - CNNs can be surprisingly vulnerable to sometimes quite subtle differences between the images they encounter at deployment and those they are trained on. We investigate the role that texture plays in domain shift, and propose a novel data augmentation technique using style transfer to train CNNs that are more robust against shifts in texture. We demonstrate that this technique results in better domain transfer on several datasets, without requiring any domain specific knowledge. In collaboration with AstraZeneca, we develop an embedding space for cellular images collected in a high throughput imaging screen as part of a drug discovery project. This uses a combination of techniques to embed the images in 2D space such that similar images are nearby, for the purpose of visualization and data exploration. The images are also clustered automatically, splitting the large dataset into a smaller number of clusters that display a common phenotype. This allows biologists to quickly triage the high throughput screen, selecting a small subset of promising phenotypes for further investigation. Finally, we investigate an unusual form of domain bias that manifested in a real-world visual binary classification project for counterfeit detection. We confirm that CNNs are able to ``cheat'' the task by exploiting a strong correlation between class label and the specific camera that acquired the image, and show that this reliably occurs when the correlation is present. We also investigate the question of how exactly the CNN is able to infer camera type from image pixels, given that this is impossible to the human eye. The contributions in this thesis are of practical value to deep learning practitioners working on a variety of problems in the field of computer vision

Adapting Computer Vision Models To Limitations On Input Dimensionality And Model Complexity

When considering instances of distributed systems where visual sensors communicate with remote predictive models, data traffic is limited to the capacity of communication channels, and hardware limits the processing of collected data prior to transmission. We study novel methods of adapting visual inference to limitations on complexity and data availability at test time, wherever the aforementioned limitations exist. Our contributions detailed in this thesis consider both task-specific and task-generic approaches to reducing the data requirement for inference, and evaluate our proposed methods on a wide range of computer vision tasks. This thesis makes four distinct contributions: (i) We investigate multi-class action classification via two-stream convolutional neural networks that directly ingest information extracted from compressed video bitstreams. We show that selective access to macroblock motion vector information provides a good low-dimensional approximation of the underlying optical flow in visual sequences. (ii) We devise a bitstream cropping method by which AVC/H.264 and H.265 bitstreams are reduced to the minimum amount of necessary elements for optical flow extraction, while maintaining compliance with codec standards. We additionally study the effect of codec rate-quality control on the sparsity and noise incurred on optical flow derived from resulting bitstreams, and do so for multiple coding standards. (iii) We demonstrate degrees of variability in the amount of data required for action classification, and leverage this to reduce the dimensionality of input volumes by inferring the required temporal extent for accurate classification prior to processing via learnable machines. (iv) We extend the Mixtures-of-Experts (MoE) paradigm to adapt the data cost of inference for any set of constituent experts. We postulate that the minimum acceptable data cost of inference varies for different input space partitions, and consider mixtures where each expert is designed to meet a different set of constraints on input dimensionality. To take advantage of the flexibility of such mixtures in processing different input representations and modalities, we train biased gating functions such that experts requiring less information to make their inferences are favoured to others. We finally note that, our proposed data utility optimization solutions include a learnable component which considers specified priorities on the amount of information to be used prior to inference, and can be realized for any combination of tasks, modalities, and constraints on available data

Machine Learning for Instance Segmentation

Volumetric Electron Microscopy images can be used for connectomics, the study of brain connectivity at the cellular level. A prerequisite for this inquiry is the automatic identification of neural cells, which requires machine learning algorithms and in particular efficient image segmentation algorithms. In this thesis, we develop new algorithms for this task. In the first part we provide, for the first time in this field, a method for training a neural network to predict optimal input data for a watershed algorithm. We demonstrate its superior performance compared to other segmentation methods of its category. In the second part, we develop an efficient watershed-based algorithm for weighted graph partitioning, the \emph{Mutex Watershed}, which uses negative edge-weights for the first time. We show that it is intimately related to the multicut and has a cutting edge performance on a connectomics challenge. Our algorithm is currently used by the leaders of two connectomics challenges. Finally, motivated by inpainting neural networks, we create a method to learn the graph weights without any supervision