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Learning to Compare: Relation Network for Few-Shot Learning

Introduces the Relation Network, an end-to-end few-shot classifier that learns a distance metric to compare examples, and extends to zero-shot learning.

The authors present a flexible, general framework for few-shot learning, where a classifier must recognize new classes from only a few examples, and train their Relation Network (RN) end-to-end from scratch. During meta-learning it learns a deep distance metric to compare images within episodes that simulate the few-shot setting. Once trained, it classifies new-class images via relation scores against the few examples, with no further updates. The framework also extends to zero-shot learning, and on five benchmarks it provides a unified, effective approach to both tasks.

Based on: Learning to Compare: Relation Network for Few-Shot Learning · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

Combines bottom-up and top-down attention over image regions, setting new state-of-the-art on MSCOCO captioning and winning the 2017 VQA Challenge.

Top-down visual attention is common in image captioning and visual question answering (VQA) for fine-grained analysis and reasoning. This work proposes a combined bottom-up and top-down mechanism operating at the level of objects and salient image regions. A bottom-up module based on Faster R-CNN proposes regions with feature vectors, while the top-down module sets feature weightings. On the MSCOCO test server it achieves a new state-of-the-art (CIDEr 117.9, SPICE 21.5, BLEU-4 36.9), and the same method won first place in the 2017 VQA Challenge.

Based on: Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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CornerNet: Detecting Objects as Paired Keypoints

Introduces CornerNet, an anchor-free detector that finds objects as pairs of top-left and bottom-right corner keypoints with corner pooling.

CornerNet reframes object detection by detecting each bounding box as a pair of keypoints, the top-left and bottom-right corners, using a single convolutional neural network. This formulation removes the need for the anchor boxes common in prior single-stage detectors. The authors also introduce corner pooling, a new pooling layer that helps the network localize corners more accurately. CornerNet reaches 42.2% AP on MS COCO, outperforming all existing one-stage detectors.

Based on: CornerNet: Detecting Objects as Paired Keypoints · International Journal of Computer Vision

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Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

Proposes weighing multi-task losses by each task's homoscedastic uncertainty, enabling joint depth, semantic, and instance segmentation.

This paper observes that multi-task deep learning performance depends heavily on the relative weighting of each task's loss, which is difficult and costly to tune by hand. It proposes a principled method that weighs multiple loss functions using the homoscedastic uncertainty of each task, enabling simultaneous learning of quantities with different units and scales across classification and regression. Applied to per-pixel depth regression, semantic and instance segmentation from a single image, the model learns its own weightings and outperforms separately trained models.

Based on: Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition

Describes the CoNLL-2003 shared task on language-independent named entity recognition, its English and German datasets, evaluation, and systems.

This paper describes the CoNLL-2003 shared task on language-independent named entity recognition. It provides background on the two data sets used, for English and German, and explains the evaluation method applied to the task. The authors give a general overview of the systems that participated and discuss their performance.

Based on: Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition · Conference on Computational Natural Language Learning

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Self-Attention Generative Adversarial Networks

Introduces SAGAN, adding self-attention and spectral normalization to GANs for long-range dependency modeling in image generation.

The Self-Attention Generative Adversarial Network (SAGAN) introduces attention-driven, long-range dependency modeling for image generation. Unlike convolutional GANs that build details from spatially local points, SAGAN generates details using cues from all feature locations, and its discriminator checks consistency across distant image regions. Applying spectral normalization to the generator further improves training. SAGAN raises the Inception score from 36.8 to 52.52 and lowers FID from 27.62 to 18.65 on ImageNet.

Based on: Self-Attention Generative Adversarial Networks · International Conference on Machine Learning

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A large annotated corpus for learning natural language inference

Introduces SNLI, a 570K-pair human-annotated corpus for natural language inference, two orders of magnitude larger than prior resources.

Understanding entailment and contradiction is fundamental to language understanding and a testbed for semantic representations, but progress was limited by small datasets. The authors introduce the Stanford Natural Language Inference (SNLI) corpus, a freely available collection of human-labeled sentence pairs from a grounded task based on image captioning. At 570K pairs it is two orders of magnitude larger than prior resources, letting lexicalized classifiers beat some sophisticated entailment models and letting a neural network perform competitively for the first time.

Based on: A large annotated corpus for learning natural language inference · Conference on Empirical Methods in Natural Language Processing

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Stochastic Processes

Introduces stochastic processes as probabilistic models of data streams and their mathematical basis for science and engineering.

This work presents stochastic processes as probabilistic models of data streams, including speech, audio and video signals, stock market prices, and measurements of physical phenomena captured by digital sensors like medical instruments, GPS receivers, or seismographs. It stresses that understanding the mathematical basis of these models is essential for interpreting phenomena and processing information across many fields of science and engineering, such as physics, communications, signal processing, automation, and structural dynamics.

Based on: Stochastic Processes · Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics

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Instance Normalization: The Missing Ingredient for Fast Stylization

Shows that swapping batch normalization for instance normalization at train and test time markedly improves fast neural image stylization.

This paper revisits a fast image stylization method and shows that a small architectural change yields a significant qualitative improvement in generated images. The change is simply swapping batch normalization for instance normalization, applied at both training and testing time. This modification enables training of high-performance architectures for real-time image generation, and the code is released publicly.

Based on: Instance Normalization: The Missing Ingredient for Fast Stylization · arXiv.org

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Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization

Produces a reliable, publicly available intrusion detection dataset with benign and seven common attack flows, and evaluates features and ML algorithms.

Growing networks raise the potential damage from attacks, making intrusion detection and prevention systems vital defenses. Anomaly-based approaches suffer from a lack of adequate datasets: reviewing eleven datasets since 1998, the authors find many outdated or unreliable, lacking traffic diversity, attack variety, or full feature sets. This paper produces a reliable, publicly available dataset with benign traffic and seven common attack flows, then evaluates traffic features and machine learning algorithms to identify the best features for detecting each attack category.

Based on: Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization · International Conference on Information Systems Security and Privacy

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A theory of learning from different domains

Develops a theory of domain adaptation, bounding a classifier's target error via source error and a measurable divergence between domains.

This work develops theory for domain adaptation, where a classifier trained on a source domain must perform well on a differently distributed target domain with little or no labeled data. It bounds target error in terms of source error and a classifier-induced divergence measure that can be estimated from finite unlabeled samples. Assuming a hypothesis performs well in both domains, these quantities characterize target error. It also bounds the error of models minimizing a convex combination of source and target errors, showing how to choose the optimal weighting.

Based on: A theory of learning from different domains · Machine-mediated learning

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Object Detection With Deep Learning: A Review

Reviews deep learning-based object detection frameworks, covering CNN architectures, training tricks, specific detection tasks, and future directions.

This survey reviews deep learning-based object detection frameworks, contrasting them with traditional methods built on handcrafted features and shallow architectures. It begins with the history of deep learning and convolutional neural networks, then examines typical generic detection architectures along with modifications and tricks that improve performance. The authors also survey specific tasks such as salient object, face, and pedestrian detection, provide experimental comparisons, and outline promising future directions.

Based on: Object Detection With Deep Learning: A Review · IEEE Transactions on Neural Networks and Learning Systems

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