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Daily AI Papers and Codes

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@Paperswithcode : Data Programming: Creating Large Training Sets, Quickly Large labeled training sets are the critical building blocks of supervised learning methods and are key enablers of deep learning techniques. For some applications, creating labeled training sets is the most time-consuming and expensive part of applying machine learning... We therefore propose a paradigm for the programmatic creation of training sets called data programming in which users express weak supervision strategies or domain heuri

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@Paperswithcode : FoveaBox: Beyond Anchor-based Object Detector We present FoveaBox, an accurate, flexible and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize the predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors... Instead, FoveaBox directly learns the object existing possibility and the bounding box coo

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@Paperswithcode : D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation Assessing the location and extent of lesions caused by chronic stroke is critical for medical diagnosis, surgical planning, and prognosis. In recent years, with the rapid development of 2D and 3D convolutional neural networks (CNN), the encoder-decoder structure has shown great potential in the field of medical image segmentation... However, the 2D CNN ignores the 3D information of medical images, while the 3D CNN

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@Paperswithcode : Deep Generalized Max Pooling Global pooling layers are an essential part of Convolutional Neural Networks (CNN). They are used to aggregate activations of spatial locations to produce a fixed-size vector in several state-of-the-art CNNs... Global average pooling or global max pooling are commonly used for converting convolutional features of variable size images to a fix-sized embedding. However, both pooling layer types are computed spatially independent: each individual activation map is pool

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@Paperswithcode : XLNet: Generalized Autoregressive Pretraining for Language Understanding With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling. However, relying on corrupting the input with masks, BERT neglects dependency between the masked positions and suffers from a pretrain-finetune discrepancy... In light of these pros and cons, we propose XLNet, a generali

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@Paperswithcode : On the Variance of the Adaptive Learning Rate and Beyond The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Here, we study its mechanism in details... Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate (i.e., it has problematically large variance in the early stage), suggest warmup works as a varianc

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@Paperswithcode : A Translate-Edit Model for Natural Language Question to SQL Query Generation on Multi-relational Healthcare Data Electronic health record (EHR) data contains most of the important patient health information and is typically stored in a relational database with multiple tables. One important way for doctors to make use of EHR data is to retrieve intuitive information by posing a sequence of questions against it... However, due to a large amount of information stored in it, effectively retrieving

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@Paperswithcode : Temporal Attentive Alignment for Large-Scale Video Domain Adaptation Although various image-based domain adaptation (DA) techniques have been proposed in recent years, domain shift in videos is still not well-explored. Most previous works only evaluate performance on small-scale datasets which are saturated... Therefore, we first propose two large-scale video DA datasets with much larger domain discrepancy: UCF-HMDB_full and Kinetics-Gameplay. Second, we investigate different DA integration met

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@Paperswithcode : Neural Blind Deconvolution Using Deep Priors Blind deconvolution is a classical yet challenging low-level vision problem with many real-world applications. Traditional maximum a posterior (MAP) based methods rely heavily on fixed and handcrafted priors that certainly are insufficient in characterizing clean images and blur kernels, and usually adopt specially designed alternating minimization to avoid trivial solution... In contrast, existing deep motion deblurring networks learn from massive t

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@Paperswithcode : SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition Understanding the spatial relations between objects in images is a surprisingly challenging task. A chair may be "behind" a person even if it appears to the left of the person in the image (depending on which way the person is facing)... Two students that appear close to each other in the image may not in fact be "next to" each other if there is a third student between them. We introduce SpatialSense, a datas

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