Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-pseudo Regularized Label for Generated Samples in Person Re-Identification

Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for labelling large number of images, the amount of available training data is always limited. To produce more data for training a deep network, Generative Adversarial Network (GAN) can be used to generate artificial sample data. However, the generated data usually does not have annotation labels. To solve this problem, in this paper, we propose a virtual label called Multi-pseudo Regularized Label (MpRL) and assign it to the generated data. With MpRL, the generated data will be used as the supplementary of real training data to train a deep neural network in a semi-supervised learning fashion. To build the corresponding relationship between the real data and generated data, MpRL assigns each generated data a proper virtual label which reflects the likelihood of the affiliation of the generated data to pre-defined training classes in the real data domain. Unlike the traditional label which usually is a single integral number, the virtual label proposed in this work is a set of weight-based values each individual of which is a number in (0,1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data. A comprehensive evaluation is carried out by adopting two state-of-the-art convolutional neural networks (CNNs) in our experiments to verify the effectiveness of MpRL. Experiments demonstrate that by assigning MpRL to generated data, we can further improve the person re-ID performance on five re-ID datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, VIPeR, and CUHK01. The proposed method obtains +6.29%, +6.30%, +5.58%, +5.84%, and +3.48% improvements in rank-1 accuracy over a strong CNN baseline on the five datasets respectively, and outperforms state-of-the-art methods.
DeepDefense: Training Deep Neural Networks with Improved Robustness

Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in security-critical systems. Recent works have shown the possibility of generating imperceptibly perturbed image inputs (a.k.a., adversarial examples) to fool well-trained DNN models into making arbitrary predictions. To address this problem, we propose a training recipe named DeepDefense. Our core idea is to integrate an adversarial perturbation-based regularizer into the classification objective, such that the obtained models learn to resist potential attacks, directly and precisely. The whole optimization problem is solved just like training a recursive network. Experimental results demonstrate that our method outperforms the state-of-the-arts by large margins on various datasets (including MNIST, CIFAR-10 and ImageNet) and different DNN architectures. Code and models for reproducing our results will be made publicly available.
An Abstractive approach to Question Answering

Question Answering has come a long way from answer sentence selection, relational QA to reading and comprehension. We move our attention to abstractive question answering by which we facilitate machine to read passages and answer questions by generating them. We frame the problem as a sequence to sequence learning where the encoder being a network that models the relation between question and passage, thereby relying solely on passage and question content to form an abstraction of the answer. Not being able to retain facts and making repetitions are common mistakes that affect the overall legibility of answers. To counter these issues, we employ copying mechanism and maintenance of coverage vector in our model respectively. Our results on MS-MARCO demonstrates it's superiority over baselines and we also show qualitative examples where we improved in terms of correctness and readability.
FRC: a high-performance concurrent parallel deferred reference counter for C++

We present FRC, a high-performance concurrent parallel reference counter for unmanaged languages. It is well known that high-performance garbage collectors help developers write memory-safe, highly concurrent systems and data structures. While C++, C, and other unmanaged languages are used in high-performance applications, adding concurrent memory management to these languages has proven to be difficult. Unmanaged languages like C++ use pointers instead of references, and have uncooperative mutators which do not pause easily at a safe point. Thus, scanning mutator stack root references is challenging. FRC only defers decrements and does not require mutator threads to pause during collection. By deferring only decrements, FRC avoids much of the synchronization overhead of a fully-deferred implementation. Root references are scanned without interrupting the mutator by publishing these references to a thread-local array. FRC's performance can exceed that of the C++ standard library's shared pointer by orders of magnitude. FRC's thread-safety guarantees and low synchronization overhead enable significant throughput gains for concurrently-readable shared data structures. We describe the components of FRC, including our static tree router data structure: a novel barrier which improves the scalability of parallel collection workers. FRC's performance is evaluated on several concurrent data structures. We release FRC and our tests as open-source code and expect FRC will be useful for many concurrent C++ software systems.
Does Higher Order LSTM Have Better Accuracy in Chunking and Named Entity Recognition?

Current researches usually employ single order setting by default when dealing with sequence labeling tasks. In our work, "order" means the number of tags that a prediction involves at every time step. High order models tend to capture more dependency information among tags. We first propose a simple method that low order models can be easily extended to high order models. To our surprise, the high order models which are supposed to capture more dependency information behave worse when increasing the order. We suppose that forcing neural networks to learn complex structure may lead to overfitting. To deal with the problem, we propose a method which combines low order and high order information together to decode the tag sequence. The proposed method, multi-order decoding (MOD), keeps the scalability to high order models with a pruning technique. MOD achieves higher accuracies than existing methods of single order setting. It results in a 21% error reduction compared to baselines in chunking and an error reduction over 23% in two NER tasks. The code is available at this https URL
ADC: Automated Deep Compression and Acceleration with Reinforcement Learning

Model compression is an effective technique facilitating the deployment of neural network models on mobile devices that have limited computation resources and a tight power budget. However, conventional model compression techniques use hand-crafted features and require domain experts to explore the large design space trading off model size, speed, and accuracy, which is usually sub-optimal and time-consuming. In this paper, we propose Automated Deep Compression (ADC) that leverages reinforcement learning in order to efficiently sample the design space and greatly improve the model compression quality. We achieved state-of-the-art model compression results in a fully automated way without any human efforts. Under 4x FLOPs reduction, we achieved 2.7% better accuracy than hand-crafted model compression method for VGG-16 on ImageNet. We applied this automated, push-the-button compression pipeline to MobileNet and achieved a 2x reduction in FLOPs, and a speedup of 1.49x on Titan Xp and 1.65x on an Android phone (Samsung Galaxy S7), with negligible loss of accuracy.
Analysis of Cause-Effect Inference via Regression Errors

We address the problem of inferring the causal relation between two variables by comparing the least-squares errors of the predictions in both possible causal directions. Under the assumption of an independence between the function relating cause and effect, the conditional noise distribution, and the distribution of the cause, we show that the errors are smaller in causal direction if both variables are equally scaled and the causal relation is close to deterministic. Based on this, we provide an easily applicable algorithm that only requires a regression in both possible causal directions and a comparison of the errors. The performance of the algorithm is compared with different related causal inference methods in various artificial and real-world data sets.
Progressive Reasoning by Module Composition

Humans learn to solve tasks of increasing complexity by building on top of previously acquired knowledge. Typically, there exists a natural progression in the tasks that we learn - most do not require completely independent solutions, but can be broken down into simpler subtasks. We propose to represent a solver for each task as a neural module that calls existing modules (solvers for simpler tasks) in a program-like manner. Lower modules are a black box to the calling module, and communicate only via a query and an output. Thus, a module for a new task learns to query existing modules and composes their outputs in order to produce its own output. Each module also contains a residual component that learns to solve aspects of the new task that lower modules cannot solve. Our model effectively combines previous skill-sets, does not suffer from forgetting, and is fully differentiable. We test our model in learning a set of visual reasoning tasks, and demonstrate state-of-the-art performance in Visual Question Answering, the highest-level task in our task set. By evaluating the reasoning process using non-expert human judges, we show that our model is more interpretable than an attention-based baseline.
Structured Probabilistic Pruning for Deep Convolutional Neural Network Acceleration

Although deep Convolutional Neural Network (CNN) has shown better performance in various machine learning tasks, its application is accompanied by a significant increase in storage and computation. Among CNN simplification techniques, parameter pruning is a promising approach which aims at reducing the number of weights of various layers without intensively reducing the original accuracy. In this paper, we propose a novel progressive parameter pruning method, named Structured Probabilistic Pruning (SPP), which efficiently prunes weights of convolutional layers in a probabilistic manner. Unlike existing deterministic pruning approaches, in which the pruned weights of a well-trained model are permanently eliminated, SPP utilizes the relative importance of weights during training iterations, which makes the pruning procedure more accurate by leveraging the accumulated weight importance. Specifically, we introduce an effective weight competition mechanism to emphasize the important weights and gradually undermine the unimportant ones. Experiments indicate that our proposed method has obtained superior performance on ConvNet and AlexNet compared with existing pruning methods. Our pruned AlexNet achieves 4.0 $\sim$ 8.9x (averagely 5.8x) layer-wise speedup in convolutional layers with only 1.3\% top-5 error increase on the ImageNet-2012 validation dataset. We also prove the effectiveness of our method on transfer learning scenarios using AlexNet.
The impact of the peer review process evolution on learner performance in e-learning environments

Student performance over a course of an academic program can be significantly affected and positively influenced through a series of feedback processes by peers and tutors. Ideally, this feedback is structured and incremental, and as a consequence, data presents at large scale even in relatively small classes. In this paper, we investigate the effect of such processes as we analyze assessment data collected from online courses. We plan to fully analyze the massive dataset of over three and a half million granular data points generated to make the case for the scalability of these kinds of learning analytics. This could shed crucial light on assessment mechanism in MOOCs, as we continue to refine our processes in an effort to strike a balance of emphasis on formative in addition to summative assessment.
Learning Nested Sparse Structures in Deep Neural Networks

Recently, there have been increasing demands to construct compact deep architectures to remove unnecessary redundancy and to improve the inference speed. While many recent works focus on reducing the redundancy by eliminating unneeded weight parameters, it is not possible to apply a single deep architecture for multiple devices with different resources. When a new device or circumstantial condition requires a new deep architecture, it is necessary to construct and train a new network from scratch. In this work, we propose a novel deep learning framework, called a nested sparse network, which exploits an n-in-1-type nested structure in a neural network. A nested sparse network consists of multiple levels of networks with a different sparsity ratio associated with each level, and higher level networks share parameters with lower level networks to enable stable nested learning. The proposed framework realizes a resource-aware versatile architecture as the same network can meet diverse resource requirements. Moreover, the proposed nested network can learn different forms of knowledge in its internal networks at different levels, enabling multiple tasks using a single network, such as coarse-to-fine hierarchical classification. In order to train the proposed nested sparse network, we propose efficient weight connection learning and channel and layer scheduling strategies. We evaluate our network in multiple tasks, including adaptive deep compression, knowledge distillation, and learning class hierarchy, and demonstrate that nested sparse networks perform competitively, but more efficiently, than existing methods.
Adaptive Convolutional Filter Generation for Natural Language Understanding

Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP are not expressive enough, in the sense that all input sentences share the same learned (and static) set of filters. Motivated by this problem, we propose an adaptive convolutional filter generation framework for natural language understanding, by leveraging a meta network to generate input-aware filters. We further generalize our framework to model question-answer sentence pairs and propose an adaptive question answering (AdaQA) model; a novel two-way feature abstraction mechanism is introduced to encapsulate co-dependent sentence representations. We investigate the effectiveness of our framework on document categorization and answer sentence-selection tasks, achieving state-of-the-art performance on several benchmark datasets.
Querying Complex Networks in Vector Space

Learning vector embeddings of complex networks is a powerful approach used to predict missing or unobserved edges in network data. However, an open challenge in this area is developing techniques that can reason about $\textit{subgraphs}$ in network data, which can involve the logical conjunction of several edge relationships. Here we introduce a framework to make predictions about conjunctive logical queries---i.e., subgraph relationships---on heterogeneous network data. In our approach, we embed network nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. We prove that a small set of geometric operations are sufficient to represent conjunctive logical queries on a network, and we introduce a series of increasingly strong implementations of these operators. We demonstrate the utility of this framework in two application studies on networks with millions of edges: predicting unobserved subgraphs in a network of drug-gene-disease interactions and in a network of social interactions derived from a popular web forum. These experiments demonstrate how our framework can efficiently make logical predictions such as "what drugs are likely to target proteins involved with both diseases X and Y? " Together our results highlight how imposing logical structure can make network embeddings more useful for large-scale knowledge discovery.
Bridging Source and Target Word Embeddings for Neural Machine Translation

Neural machine translation systems encode a source sequence into a vector from which a target sequence is generated via a decoder. Different from the traditional statistical machine translation, source and target words are not directly mapped to each other in translation rules. They are at the two ends of a long information channel in the encoder-decoder neural network, separated by source and target hidden states. This may lead to translations with inconceivable word alignments. In this paper, we try to bridge source and target word embeddings so as to shorten their distance. We propose three strategies to bridge them: 1) a source state bridging model that moves source word embeddings one step closer to their counterparts, 2) a target state bridging model that explores relevant source word embeddings for target state prediction, and 3) a direct link bridging model that directly connects source and target word embeddings so as to minimize their discrepancy. Experiments and analysis demonstrate that the proposed bridging models are able to significantly improve quality of both translation and word alignments.
SemRe-Rank: Incorporating Semantic Relatedness to Improve Automatic Term Extraction Using Personalized PageRank

Automatic Term Extraction deals with the extraction of terminology from a domain specific corpus, and has long been an established research area in data and knowledge acquisition. ATE remains a challenging task as it is known that no existing methods can consistently outperforms others in all domains. This work adopts a different strategy towards this problem as we propose to 'enhance' existing ATE methods instead of 'replace' them. We introduce SemRe-Rank, a generic method based on the concept of incorporating semantic relatedness - an often overlooked venue - into an existing ATE method to further improve its performance. SemRe-Rank applies a personalized PageRank process to a semantic relatedness graph of words to compute their 'semantic importance' scores, which are then used to revise the scores of term candidates computed by a base ATE algorithm. Extensively evaluated with 13 state-of-the-art ATE methods on four datasets of diverse nature, it is shown to have achieved widespread improvement over all methods and across all datasets. The best performing variants of SemRe-Rank have achieved, on some datasets, an improvement of 0.15 (on a scale of 0 ~ 1.0) in terms of the precision in the top ranked K term candidates, and an improvement of 0.28 in terms of overall F1.
Image Inpainting using Multi-Scale Feature Image Translation

We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents. To this end, we propose a learning-based approach to generate visually coherent completion given a high-resolution image with missing components. In order to overcome the difficulty to directly learn the distribution of high-dimensional image data, we divide the task into initialization and texture-refinement as two separate steps and model each step with a deep neural network. We also use simple heuristics to guide transferring of textures from boundary to the hole. We show that, by using such techniques, inpainting reduces to the problem of learning two image-feature translation functions of much smaller dimensionality. We evaluate our method on several public datasets and show that we not only generate results of comparable or better visual quality, but are orders of magnitude faster than previous state-of-the-art methods.
Network Cache Design under Stationary Requests: Challenges, Algorithms and Experiments

The design of caching algorithms to maximize hit probability has been extensively studied. However, the value of high hit probabilities can vary across contents due to differential service requirements. In this paper, we associate each content with a utility, which is a function of the corresponding content hit rate or hit probability. We formulate a cache optimization problem to maximize the sum of utilities over all contents under stationary and ergodic request process, which is non-convex in general. We find that the problem can be reformulated as a convex optimization problem if the inter-request distribution has a non-increasing hazard rate function. We provide explicit optimal solutions for some inter-request distributions, and compare the solutions to the hit-rate based (HRB) and hit-probability based (HPB) problems. We also propose distributed algorithms that not only can adapt to changes in the system with limited information but also provide solutions in a decentralized way. We find that distributed algorithms that solve HRB are more robust than distributed HPB algorithms. Informed by these results, we further propose a lightweight Poisson approximate online algorithm, which is accurate and efficient in achieving exact hit rates and hit probabilities, and also improves the aggregate utilities.
Learning Knowledge-guided Pose Grammar Machine for 3D Human Pose Estimation

In this paper, we propose a knowledge-guided pose grammar network to tackle the problem of 3D human pose estimation. Our model directly takes 2D poses as inputs and learns the generalized 2D-3D mapping function, which renders high applicability. The proposed network consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bidirectional RNNs on top of it to explicitly incorporate a set of knowledge (e.g., kinematics, symmetry, motor coordination) and thus enforce high-level constraints over human poses. In learning, we develop a pose-guided sample simulator to augment training samples in virtual camera views, which further improves the generalization ability of our model. We validate our method on public 3D human pose benchmarks and propose a new evaluation protocol working on cross-view setting to verify the generalization ability of different methods. We empirically observe that most state-of-the-arts face difficulty under such setting while our method obtains superior performance.
The Reincarnation of Grille Cipher

In order to keep the data secret, various techniques have been implemented to encrypt and decrypt the secret data. Cryptography is committed to the security of content, i.e. it cannot be restored with a given ciphertext. Steganography is to hide the existence of a communication channel in a stego. However, it has been difficult to construct a cipher (cypher) that simultaneously satisfy both channel and content security for secure communication. Inspired by the Cardan grille, this paper presents a new automated framework of grille cipher, this scheme can satisfy both content and channel security simultaneously. A simple practical cipher method called Digital Cardan Grille is proposed using semantic image inpainting. We also give the difference and connection between cryptography and steganography under this new grille cipher framework. Experimental results demonstrate the promising of the proposed method.