Reviewerly/exHarmony huggingface dataset:
Evolving deep convolutional neural networks by quantum behaved particle swarm optimization with binary encoding for image classification

Convolutional neural network (CNN) has proven effective at solving difficult image classification problems, but it can be challenging to design its architecture. We try to simplify the search process of the optimal architecture and minimize human participation, so quantum behaved Particle Swarm Optimization with binary encoding (BQPSO) is employed after analyzing the limitations of traditional Particle Swarm Optimization (PSO). To do this, we propose a novel and robust binary encoding strategy which does not require users with domain knowledge on CNN. Then a new quantum behaved evolving strategy is proposed to ensure the effectiveness of evolved CNN architectures. Finally, the performance of our algorithm is measured by the classification accuracy for several benchmark datasets commonly used in deep learning. The experimental results prove that our proposed method can achieve better performance and robustness than the traditional method. This is the first completely automatic algorithm in the area of using quantum behaved PSO to evolve CNN architectures.
Robustness of 3D Deep Learning in an Adversarial Setting

Understanding the spatial arrangement and nature of real-world objects is of paramount importance to many complex engineering tasks, including autonomous navigation. Deep learning has revolutionized state-of-the-art performance for tasks in 3D environments; however, relatively little is known about the robustness of these approaches in an adversarial setting. The lack of comprehensive analysis makes it difficult to justify deployment of 3D deep learning models in real-world, safety-critical applications. In this work, we develop an algorithm for analysis of pointwise robustness of neural networks that operate on 3D data. We show that current approaches presented for understanding the resilience of state-of-the-art models vastly overestimate their robustness. We then use our algorithm to evaluate an array of state-of-the-art models in order to demonstrate their vulnerability to occlusion attacks. We show that, in the worst case, these networks can be reduced to 0% classification accuracy after the occlusion of at most 6.5% of the occupied input space.
Simultaneous color-depth super-resolution with conditional generative adversarial networks

In this paper, color-depth conditional generative adversarial networks (CDcGAN) are proposed to resolve the problems of simultaneous color image super-resolution and depth image super-resolution in 3D videos. Firstly, a generative network is presented to leverage the mutual information of the low-resolution color image and low-resolution depth image so that they can enhance each other considering their geometric structural similarity in the same scene. Secondly, three auxiliary losses of data loss, total variation loss, and 8-connected gradient difference loss are introduced to train this generative network to ensure that the generated images are close to the real ones in addition to the adversarial loss. Finally, we study the CDcGAN and its variants. Experimental results show that the proposed approach can produce the high-quality color image and depth image from a pair of low-quality images, and it is superior to several other leading methods. Additionally, it has also been used to resolve the problems of concurrent image smoothing and edge detection, as well as the problem of HR-color-image-guided depth super-resolution to show the effectiveness and universality of the proposed method.
Efficient Hyperparameter Tuning with Grid Search for Text Categorization using kNN Approach with BM25 Similarity

Abstract In machine learning, hyperparameter tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Several approaches have been widely adopted for hyperparameter tuning, which is typically a time consuming process. We propose an efficient technique to speed up the process of hyperparameter tuning with Grid Search. We applied this technique on text categorization using kNN algorithm with BM25 similarity, where three hyperparameters need to be tuned. Our experiments show that our proposed technique is at least an order of magnitude faster than conventional tuning.
Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods

The conventional machine learning (ML) algorithms are continuously advancing and evolving at a fast-paced by introducing the novel learning algorithms. ML models are continually improving using hybridization and ensemble techniques to empower computation, functionality, robustness, and accuracy aspects of modeling. Currently, numerous hybrid and ensemble ML models have been introduced. However, they have not been surveyed in a comprehensive manner. This paper presents the state of the art of novel ML models and their performance and application domains through a novel taxonomy.
Locating Objects Without Bounding Boxes

Recent advances in convolutional neural networks (CNN) have achieved remarkable results in locating objects in images. In these networks, the training procedure usually requires providing bounding boxes or the maximum number of expected objects. In this paper, we address the task of estimating object locations without annotated bounding boxes which are typically hand-drawn and time consuming to label. We propose a loss function that can be used in any fully convolutional network (FCN) to estimate object locations. This loss function is a modification of the average Hausdorff distance between two unordered sets of points. The proposed method has no notion of bounding boxes, region proposals, or sliding windows. We evaluate our method with three datasets designed to locate people's heads, pupil centers and plant centers. We outperform state-of-the-art generic object detectors and methods fine-tuned for pupil tracking.
FAIM – A ConvNet Method for Unsupervised 3D Medical Image Registration

We present a new unsupervised learning algorithm, “FAIM”, for 3D medical image registration. With a different architecture than the popular “U-net” [10], the network takes a pair of full image volumes and predicts the displacement fields needed to register source to target. Compared with “U-net” based registration networks such as VoxelMorph [2], FAIM has fewer trainable parameters but can achieve higher registration accuracy as judged by Dice score on region labels in the Mindboggle-101 dataset. Moreover, with the proposed penalty loss on negative Jacobian determinants, FAIM produces deformations with many fewer “foldings”, i.e. regions of non-invertibility where the surface folds over itself. We varied the strength of this penalty and found that FAIM is able to maintain both the advantages of higher accuracy and fewer “folding” locations over VoxelMorph, over a range of hyper-parameters. We also evaluated Probabilistic VoxelMorph [3], both in its original form and with its U-net backbone replaced with our FAIM network. We found that the choice of backbone makes little difference. The original version of FAIM outperformed Probabilistic VoxelMorph for registration accuracy, and also for invertibility if FAIM is trained using an anti-folding penalty. Code for this paper is freely available at https://github.com/dykuang/Medical-image-registration .
An EDA-GA Hybrid Algorithm for Multi-Objective Task Scheduling in Cloud Computing

As one of the hot issues in cloud computing, task scheduling is an important way to meet user needs and achieve multiple goals. With the increasing number of cloud users and growing demand for cloud computing, how to reduce the task completion time and improve the system load balancing ability have attracted increasing interest from academia and industry in recent years. To meet the two aforementioned goals, this paper develops an EDA-GA hybrid scheduling algorithm based on EDA (estimation of distribution algorithm) and GA (genetic algorithm). First, the probability model and sampling method of EDA are used to generate a certain scale of feasible solutions. Second, the crossover and mutation operations of GA are used to expand the search range of solutions. Finally, the optimal scheduling strategy for assigning tasks to virtual machines is realized. This algorithm has advantages of fast convergence speed and strong search ability. The algorithm proposed in this paper is compared with EDA and GA via the CloudSim simulation experiment platform. The experimental results show that the EDA-GA hybrid algorithm can effectively reduce the task completion time and improve the load balancing ability.
Safety Verification of Cyber-Physical Systems with Reinforcement Learning Control

This paper proposes a new forward reachability analysis approach to verify safety of cyber-physical systems (CPS) with reinforcement learning controllers. The foundation of our approach lies on two efficient, exact and over-approximate reachability algorithms for neural network control systems using star sets, which is an efficient representation of polyhedra. Using these algorithms, we determine the initial conditions for which a safety-critical system with a neural network controller is safe by incrementally searching a critical initial condition where the safety of the system cannot be established. Our approach produces tight over-approximation error and it is computationally efficient, which allows the application to practical CPS with learning enable components (LECs). We implement our approach in NNV, a recent verification tool for neural networks and neural network control systems, and evaluate its advantages and applicability by verifying safety of a practical Advanced Emergency Braking System (AEBS) with a reinforcement learning (RL) controller trained using the deep deterministic policy gradient (DDPG) method. The experimental results show that our new reachability algorithms are much less conservative than existing polyhedra-based approaches. We successfully determine the entire region of the initial conditions of the AEBS with the RL controller such that the safety of the system is guaranteed, while a polyhedra-based approach cannot prove the safety properties of the system.
Modality-aware Collaborative Learning for Visible Thermal Person Re-Identification

Visible thermal person re-identification (VT-ReID) is a cross-modality pedestrian retrieval problem, which automatically searches persons between day-time visible images and night-time thermal images. Despite the extensive progress in single-modality ReID, the cross-modality pedestrian retrieval problem has limited attention due to its challenges in modality discrepancy and large intra-class variations across cameras. Existing cross-modality ReID methods usually solve this problem by learning cross-modality feature representations with modality-sharable classifier. However, this learning strategy may lose discriminative information in different modalities. In this paper, we propose a novel modality-aware collaborative (MAC) learning method on top of a two-stream network for VT-ReID, which handles the modality-discrepancy in both feature level and classifier level. In feature level, it handles the modality discrepancy by a two-stream network with different parameters. In classifier level, it contains two separate modality-specific identity classifiers for two modalities to capture the modality-specific information, and they have the same network architecture but different parameters. In addition, we introduce a collaborative learning scheme, which regularizes the modality-sharable and modality-specific identity classifiers by utilizing the relationship between different classifiers. Extensive experiments on two cross-modality person re-identification datasets demonstrate the superiority of the proposed method, achieving much better performance than the state-of-the-art.
Efficient Segmentation: Learning Downsampling Near Semantic Boundaries

Many automated processes such as auto-piloting rely on a good semantic segmentation as a critical component. To speed up performance, it is common to downsample the input frame. However, this comes at the cost of missed small objects and reduced accuracy at semantic boundaries. To address this problem, we propose a new content-adaptive downsampling technique that learns to favor sampling locations near semantic boundaries of target classes. Cost-performance analysis shows that our method consistently outperforms the uniform sampling improving balance between accuracy and computational efficiency. Our adaptive sampling gives segmentation with better quality of boundaries and more reliable support for smaller-size objects.
Zero-Shot Restoration of Back-lit Images Using Deep Internal Learning

How to restore back-lit images still remains a challenging task. State-of-the-art methods in this field are based on supervised learning and thus they are usually restricted to specific training data. In this paper, we propose a "zero-shot" scheme for back-lit image restoration, which exploits the power of deep learning, but does not rely on any prior image examples or prior training. Specifically, we train a small image-specific CNN, namely ExCNet (short for Exposure Correction Network) at test time, to estimate the "S-curve" that best fits the test back-lit image. Once the S-curve is estimated, the test image can be then restored straightforwardly. ExCNet can adapt itself to different settings per image. This makes our approach widely applicable to different shooting scenes and kinds of back-lighting conditions. Statistical studies performed on 1512 real back-lit images demonstrate that our approach can outperform the competitors by a large margin. To the best of our knowledge, our scheme is the first unsupervised CNN-based back-lit image restoration method. To make the results reproducible, the source code is available at https://cslinzhang.github.io/ExCNet/.
Recommendations and future directions for supervised machine learning in psychiatry

Abstract Machine learning methods hold promise for personalized care in psychiatry, demonstrating the potential to tailor treatment decisions and stratify patients into clinically meaningful taxonomies. Subsequently, publication counts applying machine learning methods have risen, with different data modalities, mathematically distinct models, and samples of varying size being used to train and test models with the promise of clinical translation. Consequently, and in part due to the preliminary nature of such works, many studies have reported largely varying degrees of accuracy, raising concerns over systematic overestimation and methodological inconsistencies. Furthermore, a lack of procedural evaluation guidelines for non-expert medical professionals and funding bodies leaves many in the field with no means to systematically evaluate the claims, maturity, and clinical readiness of a project. Given the potential of machine learning methods to transform patient care, albeit, contingent on the rigor of employed methods and their dissemination, we deem it necessary to provide a review of current methods, recommendations, and future directions for applied machine learning in psychiatry. In this review we will cover issues of best practice for model training and evaluation, sources of systematic error and overestimation, model explainability vs. trust, the clinical implementation of AI systems, and finally, future directions for our field.
Delving Into Robust Object Detection From Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach

Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming increasingly useful. Despite the great success of the generic object detection methods trained on ground-to-ground images, a huge performance drop is observed when they are directly applied to images captured by UAVs. The unsatisfactory performance is owing to many UAV-specific nuisances, such as varying flying altitudes, adverse weather conditions, dynamically changing viewing angles, etc. Those nuisances constitute a large number of fine-grained domains, across which the detection model has to stay robust. Fortunately, UAVs will record meta-data that depict those varying attributes, which are either freely available along with the UAV images, or can be easily obtained. We propose to utilize those free meta-data in conjunction with associated UAV images to learn domain-robust features via an adversarial training framework dubbed Nuisance Disentangled Feature Transform (NDFT), for the specific challenging problem of object detection in UAV images, achieving a substantial gain in robustness to those nuisances. We demonstrate the effectiveness of our proposed algorithm, by showing state-of-the- art performance (single model) on two existing UAV-based object detection benchmarks. The code is available at https://github.com/TAMU-VITA/UAV-NDFT.
Controllable Artistic Text Style Transfer via Shape-Matching GAN

Artistic text style transfer is the task of migrating the style from a source image to the target text to create artistic typography. Recent style transfer methods have considered texture control to enhance usability. However, controlling the stylistic degree in terms of shape deformation remains an important open challenge. In this paper, we present the first text style transfer network that allows for real-time control of the crucial stylistic degree of the glyph through an adjustable parameter. Our key contribution is a novel bidirectional shape matching framework to establish an effective glyph-style mapping at various deformation levels without paired ground truth. Based on this idea, we propose a scale-controllable module to empower a single network to continuously characterize the multi-scale shape features of the style image and transfer these features to the target text. The proposed method demonstrates its superiority over previous state-of-the-arts in generating diverse, controllable and high-quality stylized text.
Make a Face: Towards Arbitrary High Fidelity Face Manipulation

Recent studies have shown remarkable success in face manipulation task with the advance of GANs and VAEs paradigms, but the outputs are sometimes limited to low-resolution and lack of diversity. In this work, we propose Additive Focal Variational Auto-encoder (AF-VAE), a novel approach that can arbitrarily manipulate high-resolution face images using a simple yet effective model and only weak supervision of reconstruction and KL divergence losses. First, a novel additive Gaussian Mixture assumption is introduced with an unsupervised clustering mechanism in the structural latent space, which endows better disentanglement and boosts multi-modal representation with external memory. Second, to improve the perceptual quality of synthesized results, two simple strategies in architecture design are further tailored and discussed on the behavior of Human Visual System (HVS) for the first time, allowing for fine control over the model complexity and sample quality. Human opinion studies and new state-of-the-art Inception Score (IS) / Frechet Inception Distance (FID) demonstrate the superiority of our approach over existing algorithms, advancing both the fidelity and extremity of face manipulation task.
Multi-scale feature fusion residual network for Single Image Super-Resolution

We have witnessed great success of Single Image Super-Resolution (SISR) with convolutional neural networks (CNNs) in recent years. However, most existing Super-Resolution (SR) networks fail to utilize the multi-scale features of low-resolution (LR) images to further improve the representation capability for more accurate SR. In addition, most of them do not exploit the hierarchical features across networks for the final reconstruction. In this paper, we propose a novel multi-scale feature fusion residual network (MSFFRN) to fully exploit image features for SISR. Based on the residual learning, we propose a multi-scale feature fusion residual block (MSFFRB) with multiple intertwined paths to adaptively detect and fuse image features at different scales. Furthermore, the outputs of each MSFFRB and the shallow features are used as the hierarchical features for global feature fusion. Finally, we recover the high-resolution image based on the fused global features. Extensive experiments on four standard benchmarks demonstrate that our MSFFRN achieves better accuracy and visually pleasing than the current state-of-the-art methods.
Align2Ground: Weakly Supervised Phrase Grounding Guided by Image-Caption Alignment

We address the problem of grounding free-form textual phrases by using weak supervision from image-caption pairs. We propose a novel end-to-end model that uses caption-to-image retrieval as a downstream task to guide the process of phrase localization. Our method, as a first step, infers the latent correspondences between regions-of-interest (RoIs) and phrases in the caption and creates a discriminative image representation using these matched RoIs. In the subsequent step, this learned representation is aligned with the caption. Our key contribution lies in building this "caption-conditioned" image encoding, which tightly couples both the tasks and allows the weak supervision to effectively guide visual grounding. We provide extensive empirical and qualitative analysis to investigate the different components of our proposed model and compare it with competitive baselines. For phrase localization, we report an improvement of 4.9% and 1.3% (absolute) over the prior state-of-the-art on the VisualGenome and Flickr30k Entities datasets. We also report results that are at par with the state-of-the-art on the downstream caption-to-image retrieval task on COCO and Flickr30k datasets.
A detection and recognition system of pointer meters in substations based on computer vision

In order to develop unattended intelligent substations, numerous automatic meter reading methods have been proposed with the invention of inspection robots. However, most methods have strict restrictions on the captured image quality, so they cannot meet the application requirements of the substation. In this paper, a versatile pointer meter detection and recognition system based on computer vision is proposed for different environments. A Faster Region-based Convolutional Network (Faster R-CNN) is first used to detect the position of the target meter and the camera is adjusted according to the detection box. Then, thanks to the feature correspondence algorithm and the Perspective Transform, the problem of specular reflection and image distortion is solved to obtain high quality images. Finally, reading can be obtained after the position of pointer detected by Hough Transform. The experimental results verify the stability and accuracy of recognition system which is proved to work well under different conditions.
Attentive Weights Generation for Few Shot Learning via Information Maximization

Few shot image classification aims at learning a classifier from limited labeled data. Generating the classification weights has been applied in many meta-learning methods for few shot image classification due to its simplicity and effectiveness. In this work, we present Attentive Weights Generation for few shot learning via Information Maximization (AWGIM), which introduces two novel contributions: i) Mutual information maximization between generated weights and data within the task; this enables the generated weights to retain information of the task and the specific query sample. ii) Self-attention and cross-attention paths to encode the context of the task and individual queries. Both two contributions are shown to be very effective in extensive experiments. Overall, AWGIM is competitive with state-of-the-art. Code is available at https://github.com/Yiluan/AWGIM.