[PDF] Top 20 Adversarial Training for Weakly Supervised Event Detection
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Adversarial Training for Weakly Supervised Event Detection
... Modern weakly supervised methods for event detection (ED) avoid time-consuming human annotation and achieve promising results by learning from auto-labeled ...large event-related ... See full document
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Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection
... Weakly Supervised Action Detection. Action detection in a weakly supervised fashion has been studied by only a few ...of training images in each training ...object ... See full document
9
Transferable Curriculum for Weakly-Supervised Domain Adaptation
... to weakly-supervised domain adaptation, extending from curriculum learning (Bengio et ...and adversarial learning (Goodfellow et ...of weakly-supervised domain adaptation: sample noises ... See full document
8
Semi supervised Learning for Automatic Prosodic Event Detection Using Co training Algorithm
... Next we evaluate the efficacy of the three sam- ple selection methods described in Section 5, namely, random, most confident, and most dif- ferent selections. Figure 4 shows the learning curves for the three selection ... See full document
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Leveraging Just a Few Keywords for Fine Grained Aspect Detection Through Weakly Supervised Co Training
... consider weakly supervised approaches for training aspect classifiers that only require the user to provide a small set of seed words ...(i.e., weakly positive indicators) for the as- pects of ... See full document
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A Weakly Supervised Bayesian Model for Violence Detection in Social Media
... Indeed, machine learning methods for classifica- tion present difficulty on short texts (Phan et al., 2008). A large body of work has been proposed for the task of topic classification of Tweets (Milne and Witten., 2008; ... See full document
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Weakly supervised pedestrian detector training by unsupervised prior learning and cue fusion in videos
... the detection performance of the proposed algorithm closely matches that of the strong ...annotate training data for the pro- posed algorithm is less than one quarter of the time taken for the strong ... See full document
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Weakly Supervised Learning for Hedge Classification in Scientific Literature
... The training and classification models we have pre- sented require the setting of two parameters: the smoothing parameter α and the number of features per sample ... See full document
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A Topic-Aware Reinforced Model for Weakly Supervised Stance Detection
... Stance detection aims to determine from a text whether its author is in favor of, against, or neutral to- wards a given ...although weakly supervised approaches have been proposed to ease the burden ... See full document
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Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention
... Table 1 compares these methods, where we can observe that the methods with document em- beddings (i.e., the last four) significantly outper- form the pure Bi-GRU method, which suggests that document-level information is ... See full document
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Human-Like Delicate Region Erasing Strategy for Weakly Supervised Detection
... the weakly super- vised detection ...solve weakly supervised detection by mining various object pro- posal or pooling strategies, which may cause redundancy and generate a coarse ... See full document
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Towards Accurate Event Detection in Social Media: A Weakly Supervised Approach for Learning Implicit Event Indicators
... a weakly supervised approach to automatically learn such event ...quality event indicator seed, and extracts an initial set of highly relevant tweets, which are likely to contain other ...of ... See full document
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Weakly Supervised Concept based Adversarial Learning for Cross lingual Word Embeddings
... unsupervised adversarial approaches show that it is possible to build a mapping matrix that aligns two sets of monolingual word embed- dings without high quality parallel data, such as a dictionary or a ... See full document
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Weakly supervised activity analysis with spatio-temporal localisation
... for training in a weakly labelled ...each training video clip, and to annotate them with spatio-temporal ...both training and testing, and thus IS-MIML and ISOC-MIML are compared to the ... See full document
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Adversarial Learning for Weakly-Supervised Social Network Alignment
... 1,0000 training batches, we will save a check point model, and finally we can obtain 100 check points ...of training batches, the WD decreases while the Hit- P recision score ... See full document
8
Sentence Subjectivity Detection with Weakly Supervised Learning
... subjectivity detection LDA Model (subjLDA) for sentence-level subjectivity ...as weakly-supervised generative model learning where the only input to the model is a small amount of domain independent ... See full document
9
Semi-supervised adversarial variational autoencoder
... For the reconstruction optimization function, we adopt the method proposed by Gatys [56] which consists in extracting the style of an image from the first hidden layers of a pre-trained network. It is also the same ... See full document
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Weakly supervised evidence pinpointing and description
... is weakly-supervised requiring only the pathological labels and no other prior ...with training on additional weakly annotated data, which gives robust localisation with average error within 2 ... See full document
13
Weakly Supervised Definition Extraction
... As mentioned above, T S is a manually labelled dataset where each sentence s ∈ S is given a la- bel d ∈ D = {def, nodef }. Likewise, DS is an unlabelled subset of the ACL-ARC corpus, which amounts to 500k sentences. The ... See full document
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Weakly supervised learning of allomorphy
... have very similar performance, which can be ex- plained by the fact that the algorithms themselves are quite similar. However, the R-W algorithm seems to deliver somewhat superior performance. One possible reason for ... See full document
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