scholarly journals Hard Mixtures of Experts for Large Scale Weakly Supervised Vision

2017 ◽  
Author(s):  
Sam Gross ◽  
Marc'Aurelio Ranzato ◽  
Arthur Szlam
Keyword(s):  
Large Scale ◽  
Mixtures Of Experts ◽  
Weakly Supervised

scholarly journals WS-AM: Weakly Supervised Attention Map for Scene Recognition

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1072 ◽  
Author(s):  
Shifeng Xia ◽  
Jiexian Zeng ◽  
Lu Leng ◽  
Xiang Fu
Keyword(s):  
Large Scale ◽  
Multiple Scales ◽  
Scene Recognition ◽  
Small Scale ◽  
Great Success ◽  
Global Feature ◽  
Benchmark Datasets ◽  
Local Mean ◽  
Weakly Supervised ◽  
Fully Connected

Recently, convolutional neural networks (CNNs) have achieved great success in scene recognition. Compared with traditional hand-crafted features, CNN can be used to extract more robust and generalized features for scene recognition. However, the existing scene recognition methods based on CNN do not sufficiently take into account the relationship between image regions and categories when choosing local regions, which results in many redundant local regions and degrades recognition accuracy. In this paper, we propose an effective method for exploring discriminative regions of the scene image. Our method utilizes the gradient-weighted class activation mapping (Grad-CAM) technique and weakly supervised information to generate the attention map (AM) of scene images, dubbed WS-AM—weakly supervised attention map. The regions, where the local mean and the local center value are both large in the AM, correspond to the discriminative regions helpful for scene recognition. We sampled discriminative regions on multiple scales and extracted the features of large-scale and small-scale regions with two different pre-trained CNNs, respectively. The features from two different scales were aggregated by the improved vector of locally aggregated descriptor (VLAD) coding and max pooling, respectively. Finally, the pre-trained CNN was used to extract the global feature of the image in the fully- connected (fc) layer, and the local features were combined with the global feature to obtain the image representation. We validated the effectiveness of our method on three benchmark datasets: MIT Indoor 67, Scene 15, and UIUC Sports, and obtained 85.67%, 94.80%, and 95.12% accuracy, respectively. Compared with some state-of-the-art methods, the WS-AM method requires fewer local regions, so it has a better real-time performance.

scholarly journals Causal Knowledge Extraction through Large-Scale Text Mining

2020 ◽  
Vol 34 (09) ◽  
pp. 13610-13611
Author(s):  
Oktie Hassanzadeh ◽  
Debarun Bhattacharjya ◽  
Mark Feblowitz ◽  
Kavitha Srinivas ◽  
Michael Perrone ◽  
...  
Keyword(s):  
Large Scale ◽  
Causal Relation ◽  
Relation Extraction ◽  
Commercial Application ◽  
Causal Knowledge ◽  
Text Documents ◽  
Semantic Constraints ◽  
Enterprise Risk ◽  
Supervised Methods ◽  
Weakly Supervised

In this demonstration, we present a system for mining causal knowledge from large corpuses of text documents, such as millions of news articles. Our system provides a collection of APIs for causal analysis and retrieval. These APIs enable searching for the effects of a given cause and the causes of a given effect, as well as the analysis of existence of causal relation given a pair of phrases. The analysis includes a score that indicates the likelihood of the existence of a causal relation. It also provides evidence from an input corpus supporting the existence of a causal relation between input phrases. Our system uses generic unsupervised and weakly supervised methods of causal relation extraction that do not impose semantic constraints on causes and effects. We show example use cases developed for a commercial application in enterprise risk management.

scholarly journals Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity

2020 ◽  
pp. 1-51
Author(s):  
Ivan Vulić ◽  
Simon Baker ◽  
Edoardo Maria Ponti ◽  
Ulla Petti ◽  
Ira Leviant ◽  
...  
Keyword(s):  
Semantic Similarity ◽  
Large Scale ◽  
Representation Learning ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lexical Representations ◽  
Language Data ◽  
Weakly Supervised ◽  
Cross Lingual

We introduce Multi-SimLex, a large-scale lexical resource and evaluation benchmark covering data sets for 12 typologically diverse languages, including major languages (e.g., Mandarin Chinese, Spanish, Russian) as well as less-resourced ones (e.g., Welsh, Kiswahili). Each language data set is annotated for the lexical relation of semantic similarity and contains 1,888 semantically aligned concept pairs, providing a representative coverage of word classes (nouns, verbs, adjectives, adverbs), frequency ranks, similarity intervals, lexical fields, and concreteness levels. Additionally, owing to the alignment of concepts across languages, we provide a suite of 66 crosslingual semantic similarity data sets. Because of its extensive size and language coverage, Multi-SimLex provides entirely novel opportunities for experimental evaluation and analysis. On its monolingual and crosslingual benchmarks, we evaluate and analyze a wide array of recent state-of-the-art monolingual and crosslingual representation models, including static and contextualized word embeddings (such as fastText, monolingual and multilingual BERT, XLM), externally informed lexical representations, as well as fully unsupervised and (weakly) supervised crosslingual word embeddings. We also present a step-by-step data set creation protocol for creating consistent, Multi-Simlex -style resources for additional languages.We make these contributions—the public release of Multi-SimLex data sets, their creation protocol, strong baseline results, and in-depth analyses which can be be helpful in guiding future developments in multilingual lexical semantics and representation learning—available via aWeb site that will encourage community effort in further expansion of Multi-Simlex to many more languages. Such a large-scale semantic resource could inspire significant further advances in NLP across languages.

scholarly journals A Weakly Supervised and Deep Learning Method for an Additive Topic Analysis of Large Corpora

2019 ◽  
Author(s):  
Yair Fogel-Dror ◽  
Shaul R. Shenhav ◽  
Tamir Sheafer
Keyword(s):  
Content Analysis ◽  
Deep Learning ◽  
Text Classification ◽  
Large Scale ◽  
Low Cost ◽  
Initial Number ◽  
Analysis Method ◽  
Training Set ◽  
Topic Analysis ◽  
Weakly Supervised

The collaborative effort of theory-driven content analysis can benefit significantly from the use of topic analysis methods, which allow researchers to add more categories while developing or testing a theory. This additive approach enables the reuse of previous efforts of analysis or even the merging of separate research projects, thereby making these methods more accessible and increasing the discipline’s ability to create and share content analysis capabilities. This paper proposes a weakly supervised topic analysis method that uses both a low-cost unsupervised method to compile a training set and supervised deep learning as an additive and accurate text classification method. We test the validity of the method, specifically its additivity, by comparing the results of the method after adding 200 categories to an initial number of 450. We show that the suggested method provides a foundation for a low-cost solution for large-scale topic analysis.

scholarly journals A Weakly Supervised and Deep Learning Method for an Additive Topic Analysis of Large Corpora

2021 ◽  
Vol 3 (1) ◽  
pp. 29-59
Author(s):  
Yair Fogel-Dror ◽  
Shaul R. Shenhav ◽  
Tamir Sheafer
Keyword(s):  
Content Analysis ◽  
Deep Learning ◽  
Text Classification ◽  
Large Scale ◽  
Low Cost ◽  
Initial Number ◽  
Analysis Method ◽  
Training Set ◽  
Topic Analysis ◽  
Weakly Supervised

Abstract The collaborative effort of theory-driven content analysis can benefit significantly from the use of topic analysis methods, which allow researchers to add more categories while developing or testing a theory. This additive approach enables the reuse of previous efforts of analysis or even the merging of separate research projects, thereby making these methods more accessible and increasing the discipline’s ability to create and share content analysis capabilities. This paper proposes a weakly supervised topic analysis method that uses both a low-cost unsupervised method to compile a training set and supervised deep learning as an additive and accurate text classification method. We test the validity of the method, specifically its additivity, by comparing the results of the method after adding 200 categories to an initial number of 450. We show that the suggested method provides a foundation for a low-cost solution for large-scale topic analysis.

scholarly journals CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images

2018 ◽  
pp. 139-154 ◽  
Author(s):  
Sheng Guo ◽  
Weilin Huang ◽  
Haozhi Zhang ◽  
Chenfan Zhuang ◽  
Dengke Dong ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Large Scale ◽  
Weakly Supervised Learning ◽  
Web Images ◽  
Weakly Supervised

scholarly journals A Weakly Supervised Deep Learning Framework for Sorghum Head Detection and Counting

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Sambuddha Ghosal ◽  
Bangyou Zheng ◽  
Scott C. Chapman ◽  
Andries B. Potgieter ◽  
David R. Jordan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Genotypic Variation ◽  
Model Performance ◽  
Phenotypic Traits ◽  
Learning Framework ◽  
Detection And Counting ◽  
Head Detection ◽  
Low Efficiency ◽  
Weakly Supervised

The yield of cereal crops such as sorghum (Sorghum bicolor L. Moench) depends on the distribution of crop-heads in varying branching arrangements. Therefore, counting the head number per unit area is critical for plant breeders to correlate with the genotypic variation in a specific breeding field. However, measuring such phenotypic traits manually is an extremely labor-intensive process and suffers from low efficiency and human errors. Moreover, the process is almost infeasible for large-scale breeding plantations or experiments. Machine learning-based approaches like deep convolutional neural network (CNN) based object detectors are promising tools for efficient object detection and counting. However, a significant limitation of such deep learning-based approaches is that they typically require a massive amount of hand-labeled images for training, which is still a tedious process. Here, we propose an active learning inspired weakly supervised deep learning framework for sorghum head detection and counting from UAV-based images. We demonstrate that it is possible to significantly reduce human labeling effort without compromising final model performance (R2 between human count and machine count is 0.88) by using a semitrained CNN model (i.e., trained with limited labeled data) to perform synthetic annotation. In addition, we also visualize key features that the network learns. This improves trustworthiness by enabling users to better understand and trust the decisions that the trained deep learning model makes.

Taking the pulse of the Amazon rainforest by fusing multitemporal Sentinel 1 and 2 data for advanced deep-learning

2021 ◽  
Author(s):  
Melanie Brandmeier ◽  
Eya Cherif
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Land Cover ◽  
Supervised Learning ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Brazilian Amazon ◽  
Sensing Data ◽  
Weakly Supervised

<p>Degradation of large forest areas such as the Brazilian Amazon due to logging and fires can increase the human footprint way beyond deforestation. Monitoring and quantifying such changes on a large scale has been addressed by several research groups (e.g. Souza et al. 2013) by making use of freely available remote sensing data such as the Landsat archive. However, fully automatic large-scale land cover/land use mapping is still one of the great challenges in remote sensing. One problem is the availability of reliable “ground truth” labels for training supervised learning algorithms. For the Amazon area, several landcover maps with 22 classes are available from the MapBiomas project that were derived by semi-automatic classification and verified by extensive fieldwork (Project MapBiomas). These labels cannot be considered real ground-truth as they were derived from Landsat data themselves but can still be used for weakly supervised training of deep-learning models that have a potential to improve predictions on higher resolution data nowadays available. The term weakly supervised learning was originally coined by (Zhou 2017) and refers to the attempt of constructing predictive models from incomplete, inexact and/or inaccurate labels as is often the case in remote sensing. To this end, we investigate advanced deep-learning strategies on Sentinel-1 timeseries and Sentinel-2 optical data to improve large-scale automatic mapping and monitoring of landcover changes in the Amazon area. Sentinel-1 data has the advantage to be resistant to cloud cover that often hinders optical remote sensing in the tropics.</p><p>We propose new architectures that are adapted to the particularities of remote sensing data (S1 timeseries and multispectral S2 data) and compare the performance to state-of-the-art models.  Results using only spectral data were very promising with overall test accuracies of 77.9% for Unet and 74.7% for a DeepLab implementation with ResNet50 backbone and F1 measures of 43.2% and 44.2% respectively.  On the other hand, preliminary results for new architectures leveraging the multi-temporal aspect of  SAR data have improved the quality of mapping, particularly for agricultural classes. For instance, our new designed network AtrousDeepForestM2 has a similar quantitative performances as DeepLab  (F1 of 58.1% vs 62.1%), however it produces better qualitative land cover maps.</p><p>To make our approach scalable and feasible for others, we integrate the trained models in a geoprocessing tool in ArcGIS that can also be deployed in a cloud environment and offers a variety of post-processing options to the user.</p><p>Souza, J., Carlos M., et al. (2013). "Ten-Year Landsat Classification of Deforestation and Forest Degradation in the Brazilian Amazon." Remote Sensing 5(11): 5493-5513.   </p><p>Zhou, Z.-H. (2017). "A brief introduction to weakly supervised learning." National Science Review 5(1): 44-53.</p><p>"Project MapBiomas - Collection  4.1 of Brazilian Land Cover & Use Map Series, accessed on January 2020 through the link: https://mapbiomas.org/colecoes-mapbiomas?cama_set_language=en"</p>

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