Reviewerly/exHarmony huggingface dataset:
Safe Sample Screening for Robust Support Vector Machine

Robust support vector machine (RSVM) has been shown to perform remarkably well to improve the generalization performance of support vector machine under the noisy environment. Unfortunately, in order to handle the non-convexity induced by ramp loss in RSVM, existing RSVM solvers often adopt the DC programming framework which is computationally inefficient for running multiple outer loops. This hinders the application of RSVM to large-scale problems. Safe sample screening that allows for the exclusion of training samples prior to or early in the training process is an effective method to greatly reduce computational time. However, existing safe sample screening algorithms are limited to convex optimization problems while RSVM is a non-convex problem. To address this challenge, in this paper, we propose two safe sample screening rules for RSVM based on the framework of concave-convex procedure (CCCP). Specifically, we provide screening rule for the inner solver of CCCP and another rule for propagating screened samples between two successive solvers of CCCP. To the best of our knowledge, this is the first work of safe sample screening to a non-convex optimization problem. More importantly, we provide the security guarantee to our sample screening rules to RSVM. Experimental results on a variety of benchmark datasets verify that our safe sample screening rules can significantly reduce the computational time.
Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data

The success of training accurate models strongly depends on the availability of a sufficient collection of precisely labeled data. However, real-world datasets contain erroneously labeled data samples that substantially hinder the performance of machine learning models. Meanwhile, well-labeled data is usually expensive to obtain and only a limited amount is available for training. In this paper, we consider the problem of training a robust model by using large-scale noisy data in conjunction with a small set of clean data. To leverage the information contained via the clean labels, we propose a novel self-paced robust learning algorithm (SPRL) that trains the model in a process from more reliable (clean) data instances to less reliable (noisy) ones under the supervision of well-labeled data. The self-paced learning process hedges the risk of selecting corrupted data into the training set. Moreover, theoretical analyses on the convergence of the proposed algorithm are provided under mild assumptions. Extensive experiments on synthetic and real-world datasets demonstrate that our proposed approach can achieve a considerable improvement in effectiveness and robustness to existing methods.
MALA: Cross-Domain Dialogue Generation with Action Learning

Response generation for task-oriented dialogues involves two basic components: dialogue planning and surface realization. These two components, however, have a discrepancy in their objectives, i.e., task completion and language quality. To deal with such discrepancy, conditioned response generation has been introduced where the generation process is factorized into action decision and language generation via explicit action representations. To obtain action representations, recent studies learn latent actions in an unsupervised manner based on the utterance lexical similarity. Such an action learning approach is prone to diversities of language surfaces, which may impinge task completion and language quality. To address this issue, we propose multi-stage adaptive latent action learning (MALA) that learns semantic latent actions by distinguishing the effects of utterances on dialogue progress. We model the utterance effect using the transition of dialogue states caused by the utterance and develop a semantic similarity measurement that estimates whether utterances have similar effects. For learning semantic actions on domains without dialogue states, MALA extends the semantic similarity measurement across domains progressively, i.e., from aligning shared actions to learning domain-specific actions. Experiments using multi-domain datasets, SMD and MultiWOZ, show that our proposed model achieves consistent improvements over the baselines models in terms of both task completion and language quality.
Deep morphological simplification network (MS-Net) for guided registration of brain magnetic resonance images

Deformable brain MR image registration is challenging due to large inter-subject anatomical variation. For example, the highly complex cortical folding pattern makes it hard to accurately align corresponding cortical structures of individual images. In this paper, we propose a novel deep learning way to simplify the difficult registration problem of brain MR images. Specifically, we train a morphological simplification network (MS-Net), which can generate a simple image with less anatomical details based on the complex input. With MS-Net, the complexity of the fixed image or the moving image under registration can be reduced gradually, thus building an individual (simplification) trajectory represented by MS-Net outputs. Since the generated images at the ends of the two trajectories (of the fixed and moving images) are so simple and very similar in appearance, they are easy to register. Thus, the two trajectories can act as a bridge to link the fixed and the moving images, and guide their registration. Our experiments show that the proposed method can achieve highly accurate registration performance on different datasets (i.e., NIREP, LPBA, IBSR, CUMC, and MGH). Moreover, the method can be also easily transferred across diverse image datasets and obtain superior accuracy on surface alignment. We propose MS-Net as a powerful and flexible tool to simplify brain MR images and their registration. To our knowledge, this is the first work to simplify brain MR image registration by deep learning, instead of estimating deformation field directly.
Softmax Dissection: Towards Understanding Intra- and Inter-Class Objective for Embedding Learning

The softmax loss and its variants are widely used as objectives for embedding learning applications like face recognition. However, the intra- and inter-class objectives in Softmax are entangled, therefore a well-optimized inter-class objective leads to relaxation on the intra-class objective, and vice versa. In this paper, we propose to dissect Softmax into independent intra- and inter-class objective (D-Softmax) with a clear understanding. It is straightforward to tune each part to the best state with D-Softmax as objective.Furthermore, we find the computation of the inter-class part is redundant and propose sampling-based variants of D-Softmax to reduce the computation cost. The face recognition experiments on regular-scale data show D-Softmax is favorably comparable to existing losses such as SphereFace and ArcFace. Experiments on massive-scale data show the fast variants significantly accelerates the training process (such as 64×) with only a minor sacrifice in performance, outperforming existing acceleration methods of Softmax in terms of both performance and efficiency.
Multimodal Structure-Consistent Image-to-Image Translation

Unpaired image-to-image translation is proven quite effective in boosting a CNN-based object detector for a different domain by means of data augmentation that can well preserve the image-objects in the translated images. Recently, multimodal GAN (Generative Adversarial Network) models have been proposed and were expected to further boost the detector accuracy by generating a diverse collection of images in the target domain, given only a single/labelled image in the source domain. However, images generated by multimodal GANs would achieve even worse detection accuracy than the ones by a unimodal GAN with better object preservation. In this work, we introduce cycle-structure consistency for generating diverse and structure-preserved translated images across complex domains, such as between day and night, for object detector training. Qualitative results show that our model, Multimodal AugGAN, can generate diverse and realistic images for the target domain. For quantitative comparisons, we evaluate other competing methods and ours by using the generated images to train YOLO, Faster R-CNN and FCN models and prove that our model achieves significant improvement and outperforms other methods on the detection accuracies and the FCN scores. Also, we demonstrate that our model could provide more diverse object appearances in the target domain through comparison on the perceptual distance metric.
Hide-and-Tell: Learning to Bridge Photo Streams for Visual Storytelling

Visual storytelling is a task of creating a short story based on photo streams. Unlike existing visual captioning, storytelling aims to contain not only factual descriptions, but also human-like narration and semantics. However, the VIST dataset consists only of a small, fixed number of photos per story. Therefore, the main challenge of visual storytelling is to fill in the visual gap between photos with narrative and imaginative story. In this paper, we propose to explicitly learn to imagine a storyline that bridges the visual gap. During training, one or more photos is randomly omitted from the input stack, and we train the network to produce a full plausible story even with missing photo(s). Furthermore, we propose for visual storytelling a hide-and-tell model, which is designed to learn non-local relations across the photo streams and to refine and improve conventional RNN-based models. In experiments, we show that our scheme of hide-and-tell, and the network design are indeed effective at storytelling, and that our model outperforms previous state-of-the-art methods in automatic metrics. Finally, we qualitatively show the learned ability to interpolate storyline over visual gaps.
Knowledge Integration Networks for Action Recognition

In this work, we propose Knowledge Integration Networks (referred as KINet) for video action recognition. KINet is capable of aggregating meaningful context features which are of great importance to identifying an action, such as human information and scene context. We design a three-branch architecture consisting of a main branch for action recognition, and two auxiliary branches for human parsing and scene recognition which allow the model to encode the knowledge of human and scene for action recognition. We explore two pre-trained models as teacher networks to distill the knowledge of human and scene for training the auxiliary tasks of KINet. Furthermore, we propose a two-level knowledge encoding mechanism which contains a Cross Branch Integration (CBI) module for encoding the auxiliary knowledge into medium-level convolutional features, and an Action Knowledge Graph (AKG) for effectively fusing high-level context information. This results in an end-to-end trainable framework where the three tasks can be trained collaboratively, allowing the model to compute strong context knowledge efficiently. The proposed KINet achieves the state-of-the-art performance on a large-scale action recognition benchmark Kinetics-400, with a top-1 accuracy of 77.8%. We further demonstrate that our KINet has strong capability by transferring the Kinetics-trained model to UCF-101, where it obtains 97.8% top-1 accuracy.
Neural Question Generation with Answer Pivot

Neural question generation (NQG) is the task of generating questions from the given context with deep neural networks. Previous answer-aware NQG methods suffer from the problem that the generated answers are focusing on entity and most of the questions are trivial to be answered. The answer-agnostic NQG methods reduce the bias towards named entities and increasing the model's degrees of freedom, but sometimes result in generating unanswerable questions which are not valuable for the subsequent machine reading comprehension system. In this paper, we treat the answers as the hidden pivot for question generation and combine the question generation and answer selection process in a joint model. We achieve the state-of-the-art result on the SQuAD dataset according to automatic metric and human evaluation.
Not All Attention Is Needed: Gated Attention Network for Sequence Data

Although deep neural networks generally have fixed network structures, the concept of dynamic mechanism has drawn more and more attention in recent years. Attention mechanisms compute input-dependent dynamic attention weights for aggregating a sequence of hidden states. Dynamic network configuration in convolutional neural networks (CNNs) selectively activates only part of the network at a time for different inputs. In this paper, we combine the two dynamic mechanisms for text classification tasks. Traditional attention mechanisms attend to the whole sequence of hidden states for an input sentence, while in most cases not all attention is needed especially for long sequences. We propose a novel method called Gated Attention Network (GA-Net) to dynamically select a subset of elements to attend to using an auxiliary network, and compute attention weights to aggregate the selected elements. It avoids a significant amount of unnecessary computation on unattended elements, and allows the model to pay attention to important parts of the sequence. Experiments in various datasets show that the proposed method achieves better performance compared with all baseline models with global or local attention while requiring less computation and achieving better interpretability. It is also promising to extend the idea to more complex attention-based models, such as transformers and seq-to-seq models.
Separate in Latent Space: Unsupervised Single Image Layer Separation

Many real world vision tasks, such as reflection removal from a transparent surface and intrinsic image decomposition, can be modeled as single image layer separation. However, this problem is highly ill-posed, requiring accurately aligned and hard to collect triplet data to train the CNN models. To address this problem, this paper proposes an unsupervised method that requires no ground truth data triplet in training. At the core of the method are two assumptions about data distributions in the latent spaces of different layers, based on which a novel unsupervised layer separation pipeline can be derived. Then the method can be constructed based on the GANs framework with self-supervision and cycle consistency constraints, etc. Experimental results demonstrate its successfulness in outperforming existing unsupervised methods in both synthetic and real world tasks. The method also shows its ability to solve a more challenging multi-layer separation task.
Overcoming Catastrophic Forgetting by Neuron-Level Plasticity Control

To address the issue of catastrophic forgetting in neural networks, we propose a novel, simple, and effective solution called neuron-level plasticity control (NPC). While learning a new task, the proposed method preserves the existing knowledge from the previous tasks by controlling the plasticity of the network at the neuron level. NPC estimates the importance value of each neuron and consolidates important neurons by applying lower learning rates, rather than restricting individual connection weights to stay close to the values optimized for the previous tasks. The experimental results on the several datasets show that neuron-level consolidation is substantially more effective compared to connection-level consolidation approaches.
Training Decision Trees as Replacement for Convolution Layers

We present an alternative layer to convolution layers in convolutional neural networks (CNNs). Our approach reduces the complexity of convolutions by replacing it with binary decisions. Those binary decisions are used as indexes to conditional distributions where each weight represents a leaf in a decision tree. This means that only the indices to the weights need to be determined once, thus reducing the complexity of convolutions by the depth of the output tensor. Index computation is performed by simple binary decisions that require fewer cycles compared to conventionally used multiplications. In addition, we show how convolutions can be replaced by binary decisions. These binary decisions form indices in the conditional distributions and we show how they are used to replace 2D weight matrices as well as 3D weight tensors. These new layers can be trained like convolution layers in CNNs based on the backpropagation algorithm, for which we provide a formalization. Our results on multiple publicly available data sets show that our approach performs similar to conventional neuronal networks. Beyond the formalized reduction of complexity and the improved qualitative performance, we show the runtime improvement empirically compared to convolution layers.
Detection of Fake Currency using Image Processing

In recent years a lot of fake currency note is being printed which have caused great loss and damage towards society.So, it has become a necessity to develop a tool to detect fake currency.This project proposes an approach that will detect fake currency note being circulated in our country by using their image.Our project will provide required mobility and compatibility to most peoples as well as credible accuracy for the fake currency detection.We are using image processing and cloud storage to make this app portable and efficient.
Active and Incremental Learning with Weak Supervision

Abstract Large amounts of labeled training data are one of the main contributors to the great success that deep models have achieved in the past. Label acquisition for tasks other than benchmarks can pose a challenge due to requirements of both funding and expertise. By selecting unlabeled examples that are promising in terms of model improvement and only asking for respective labels, active learning can increase the efficiency of the labeling process in terms of time and cost. In this work, we describe combinations of an incremental learning scheme and methods of active learning. These allow for continuous exploration of newly observed unlabeled data. We describe selection criteria based on model uncertainty as well as expected model output change (EMOC). An object detection task is evaluated in a continuous exploration context on the PASCAL VOC dataset. We also validate a weakly supervised system based on active and incremental learning in a real-world biodiversity application where images from camera traps are analyzed. Labeling only 32 images by accepting or rejecting proposals generated by our method yields an increase in accuracy from 25.4 to 42.6%.
About this title - Sweden: Lithotectonic Framework, Tectonic Evolution and Mineral Resources

The solid rock mass of Sweden forms a natural field laboratory revealing insight into the westward growth and reworking of one of the planet's ancient continental nuclei. Three major geological units are exposed in different parts of the country: the western part of the Fennoscandian Shield, mainly sedimentary rocks deposited on this crystalline rock mass and the Caledonide orogen. This volume synthesizes the tectonic evolution of Sweden over more than 2500 million years from the Neoarchean to the Neogene. Following an introduction describing the lithotectonic framework of the country and the organization of the volume, the tectonic evolution is addressed essentially chronologically. Different phases of intracratonic rifting, accretionary orogeny, continent–continent collisional orogeny and platformal sedimentation are identified. Sweden is one of Europe's major suppliers of metals, and the country's mineral resources are also presented in the context of the lithotectonic framework. Sweden: Lithotectonic Framework, Tectonic Evolution and Mineral Resources has been designed to interest a professional geoscientific audience and advanced students of Earth Sciences.
Domain Adaptation for One-Class Classification: Monitoring the Health of Critical Systems Under Limited Information

The failure of a complex and safety critical industrial asset can have extremely high consequences. Close monitoring for early detection of abnormal system conditions is therefore required. Data-driven solutions to this problem have been limited for two reasons: First, safety critical assets are designed and maintained to be highly reliable and faults are rare. Fault detection can thus not be solved with supervised learning. Second, complex industrial systems usually have long lifetime during which they face very different operating conditions. In the early life of the system, the collected data is probably not representative of future operating conditions, making it challenging to train a robust model. In this paper, we propose a methodology to monitor the systems in their early life. To do so, we enhance the training dataset with other units from a fleet, for which longer observations are available. Since each unit has its own specificity, we propose to extract features made independent of their origin by three unsupervised feature alignment techniques. First, using a variational encoder, we impose a shared probabilistic encoder/decoder for both units. Second, we introduce a new loss designed to conserve inter-point spacial relationships between the input and the learned features. Last, we propose to train in an adversarial manner a discriminator on the origin of the features. Once aligned, the features are fed to a one-class classifier to monitor the health of the system. By exploring the different combinations of the proposed alignment strategies, and by testing them on a real case study, a fleet composed of 112 power plants operated in different geographical locations and under very different operating regimes, we demonstrate that this alignment is necessary and beneficial.
No-reference video quality evaluation by a deep transfer CNN architecture

The standard no-reference video quality assessment (NR-VQA) is designed for a specific type of distortion. It quantifies the visual quality of a distorted video without the reference one. Practically, there is a deviation between the result of NR-VQA and human subjective perception. To tackle this problem, we propose a 3D deep convolutional neural network (3D CNN) to evaluate video quality without reference by generating spatial/temporal deep features within different video clips 3D CNN is designed by collaboratively and seamlessly integrating the features output from VGG-Net on video frames. To prevent our adopted VGG-Net from overfitting, the parameters are transferred from the deep architecture learned from the ImageNet dataset. Extensive IQA/VQA experimental results based on the LIVE, TID, and the CSIQ video quality databases have demonstrated that the proposed IQA/VQA model performs competitively the conventional methods.