226 research outputs found
Deep Manifold Traversal: Changing Labels with Convolutional Features
Many tasks in computer vision can be cast as a "label changing" problem, where the goal is to make a semantic change to the appearance of an image or some subject in an image in order to alter the class membership. Although successful task-specific methods have been developed for some label changing applications, to date no general purpose method exists. Motivated by this we propose deep manifold traversal, a method that addresses the problem in its most general form: it first approximates the manifold of natural images then morphs a test image along a traversal path away from a source class and towards a target class while staying near the manifold throughout. The resulting algorithm is surprisingly effective and versatile. It is completely data driven, requiring only an example set of images from the desired source and target domains. We demonstrate deep manifold traversal on highly diverse label changing tasks: changing an individual's appearance (age and hair color), changing the season of an outdoor image, and transforming a city skyline towards nighttime
“Beloved Be the Ones Who Sit Down”: Aesthetics and Political Affect in Roy Andersson’s “Living” Trilogy
Roy Andersson’s unique surrealist style and the affect it gives rise to, situated somewhere between deep existential dread and the most absurdist humor, are intimately connected to his staging of action in stacked layers of meaning in deep focus, immobile long takes. A formal reading of his films then gives us a greater understanding of the connection between affect and film style.But the tableau which all but evacuates time in Andersson is not only a stylistic choice: this challenge to traditional structures and temporalities is the formal manifestation of his anachronistic conception of history. I argue that cinematic time is here closely tied to historical time: a view of history as layered, instantaneous and made up of incongruous juxtapositions as commentary on a failure of historicism as central to the development of a national Swedish identity marked by passivity, anti-intellectualism, and a lack of historical conscience
The Natural Language Generation Pipeline, Neural Text Generation and Explainability
International audienceEnd-to-end encoder-decoder approaches to data-to-text generation are often black boxes whose predictions are difficult to explain. Breaking up the end-to-end model into submodules is a natural way to address this problem. The traditional pre-neural Natural Language Generation (NLG) pipeline provides a framework for breaking up the end-to-end encoder-decoder. We survey recent papers that integrate traditional NLG sub-modules in neural approaches and analyse their explainability. Our survey is a first step towards building explainable neural NLG models
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Multi-Branch Network for Few-shot Learning
Few-shot learning aims provide precise predictions for unseen data through learning from only one or few labelled samples of each class. However, it often suffers from the overfitting problem because of insufficient training data. In this paper, we propose a novel metric-based few-shot learning method, multi-branch network (MBN), with a new data augmentation module to improve the generalization ability of the model. Specifically, we generate different types of noise contaminated data through multiple branches in the network to simulate the real-world scenarios when noisy images are obtained. Following this novel data augmentation module, the feature embedding and similarities between the support and query samples are learned simultaneously through the embedding and metric modules, respectively. Moreover, to consider more details in the feature maps, we propose to utilize the average-pooling layer in the metric module rather than the commonly adopted max-pooling layer. The network is trained from end to end by the Kullback- Leibler (KL) divergence, to minimize the difference between the distributions of the ground truths and predictions. Extensive experiments on Standford-Dogs, Standford-Cars, CUB-200-2011 and mini-ImageNet in the 1-shot and 5-shot tasks demonstrate the superior classification performance of MBN
Learning from Very Few Samples: A Survey
Few sample learning (FSL) is significant and challenging in the field of
machine learning. The capability of learning and generalizing from very few
samples successfully is a noticeable demarcation separating artificial
intelligence and human intelligence since humans can readily establish their
cognition to novelty from just a single or a handful of examples whereas
machine learning algorithms typically entail hundreds or thousands of
supervised samples to guarantee generalization ability. Despite the long
history dated back to the early 2000s and the widespread attention in recent
years with booming deep learning technologies, little surveys or reviews for
FSL are available until now. In this context, we extensively review 300+ papers
of FSL spanning from the 2000s to 2019 and provide a timely and comprehensive
survey for FSL. In this survey, we review the evolution history as well as the
current progress on FSL, categorize FSL approaches into the generative model
based and discriminative model based kinds in principle, and emphasize
particularly on the meta learning based FSL approaches. We also summarize
several recently emerging extensional topics of FSL and review the latest
advances on these topics. Furthermore, we highlight the important FSL
applications covering many research hotspots in computer vision, natural
language processing, audio and speech, reinforcement learning and robotic, data
analysis, etc. Finally, we conclude the survey with a discussion on promising
trends in the hope of providing guidance and insights to follow-up researches.Comment: 30 page
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