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Machine heart & ArXiv Weekly Radiostation

participate:Du Wei、Chu Air、Roenian

This week's important paper isCVPR 2020 Announced award winning papers,Including best papers and best student papers, etc.。

(bsp brass compression fittings to 1 4 tube)content:

  1. Knowledge Distillation: A Survey
  2. Description Based Text Classification with Reinforcement Learning
  3. Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild
  4. BSP-Net: Generating Compact Meshes via Binary Space Partitioning
  5. Generative Pretraining from Pixels
  6. ActBERT: Learning Global-Local Video-Text Representations
  7. A Survey on Dynamic Network Embedding
  8. ArXiv Weekly Radiostation:NLP、CV、ML More selected papers(Audio)
(bsp brass compression fittings to 1 4 tube)paper 1:Knowledge Distillation: A Survey

  • author:Jianping Gou、Baosheng Yu、Stephen John Maybank、Dacheng Tao
  • Papers link:https://arxiv.org/pdf/2006.05525.pdf

Summary:In recent years,Deep neural network has achieved great success in the industry and academic community,Especially in visual identification and neurological treatment applications。The huge success of deep learning is mainly due to its powerful scalability,Existing large-scale data samples,There are also billions of model parameters。But it should also be seen,Deploying these cumbersome depth models on devices such as mobile phones and embedded resources have also brought huge challenges.,This is not only because of the large amount of calculation.,And the required storage space is very large.。In order to solve these problems,Researchers have developed various model compression and acceleration technology,Twig、Quantitative and neural structure search。

Knowledge distillation is a typical model compression and acceleration method,Aiming to learn primary school models from big teachers models,Therefore, more and more researchers are attracted.。in the text,Researchers from the University of Sydney and the University of London from knowledge classification、Training program、Knowledge extraction algorithm and application, etc.。also,They also briefly review the challenges facing knowledge distillation fields.,And provide some insights for future research issues。

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General teacher with knowledge distillation - Student network framework。

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Article structure diagram。

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Specific architectural diagram of the benchmark knowledge distillation。

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Different types of distillation。

recommend:This document is a professional professor of computer science at Sydney University.(Dacheng Tao)。

paper 2:Description Based Text Classification with Reinforcement Learning

  • author:Duo Chai、Wei Wu、Qinghong Han、Wu Fei 、Jiwei Li
  • Papers link:https://arxiv.org/pdf/2002.03067.pdf

Summary:Text classification tasks are usually divided into two phases:Text feature extraction and classification。Under this standardization setting,Category only indicate the index of label words,And the model lacks an explicit description of the classified object。

therefore,In the current usual NLP Problem formulation expressed as the trend of question and answer tasks,Researchers from Xiangli Technology and Zhejiang University put forward a novel text classification framework,Each category tag and category describes each other。Text description can be manually templated(handcrafted template)Or use the strengthening learning abstract style / Extraction model to generate。Description and Cascade of text are fed into the classifier,To decide whether the current label should be classified to text。

Researchers found in single tag classification、A series of text classification tasks such as multi-label classification and multi-angle emotional analysis,The performance of strong baseline method has increased significantly。

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Description type constructed by different strategies。Text comes from 20news data set。

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AGNews、20news、DBPedia、Yahoo、Yelp P and IMDB Test error rate for single-label classification on data set。

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Reuters and AAPD Multi-label classification test error rate on data set。

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BeerAdvocate(Beer) and TripAdvisor(Trip) Multi-angle emotional classification test error rate on data set。

(bsp brass compression fittings to 1 4 tube)recommend:The strategy proposed by this study makes the model and label describes the most relevant text,Can be considered as a hard version(hard version),Thereby achieving better performance

(bsp brass compression fittings to 1 4 tube)paper 3:Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild

(bsp brass compression fittings to 1 4 tube)Summary:In the research,Researchers from Oxford University proposes a kind of original single-graphic image learning 3D New method for deformable object categories,No external supervision。This method is based on a self-encoder,It decomposes each input image to depth、Reflectivity、Viewpoint and light(Combine these four components to reconstruct the input image)。This model uses only reconstruction losses during the training,No external supervision。In order to decompose these components under the premise of supervising signals,Researchers have used the properties of many object categories.——Symmetrical structure。

This study shows,Reasoning the light can help us use the underlying symmetry of the object,Even if the shadow is such as factors, there is no relationship between the appearance of the object.。also,This study also uses model other components to learn symmetrical probability maps in terms of end-to-end way,And use the prediction of this probability map to model an asymmetric object。Experiment,This method can accurately recover the face of people in a single image、Cat face and vehicle 3D shape,There is no need for any supervision or prior shape model。Compared to utilization 2D Another method of image corresponding supervision,This method is more superior to the performance of the reference data set.。

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As an Wu Shangzhe(Shangzhe Wu),Now the second-year student of Oxford University。

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Model structure diagram。

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human face、Cat and car reconstruction rendering。

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and SOTA Qualitative comparison of methods。

(bsp brass compression fittings to 1 4 tube)recommend:This article is obtained. CVPR 2020 Best Parassment Award。

paper 4:BSP-Net: Generating Compact Meshes via Binary Space Partitioning

Summary:Polygon mesh in numbers 3D Unwanted in the field,But they only play a secondary role in the deep learning revolution.。Leading method of learning shape generation model relies on hidden functions,And can only generate grids after the expensive equivalent surface treatment process。In order to overcome these challenges,Researchers from the University of Simon Fraser and Google Research are classic spatial data structures in computer graphics Binary Space Partitioning(BSP)Inspiration,Come to promote 3D Learn。BSP The core part is a recursive segmentation of space to obtain a convex set.。

Use this property,Researchers designed BSP-Net,The network can learn through convex decomposition 3D shape。Important,BSPNet Learned without supervision,Because of the need to convex decomposition during the training。The training purpose of the network is,To use a set of flat buildings BSPtree A set of convex refactoring shapes。go through BSPNet Introduced convex surface can be easily extracted to form a polygon mesh,No equivalent surface treatment。The generated grid is compact(Low polygon),Ideal for a sharp geometry。also,They must be aqueous grid,And easily parameterize。

The study also shows,BSP-Net Refactoring quality and SOTA Method compared to competitiveness,And it is much less originally used.。

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Papers Zhiqin Chen,Now the first-year student of Simon Fraser University。

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BSP-Net Present SOTA method(IM-NET)Comparison of generation effects。

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BSP-Net Network structure。

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BSP-Net Comparison of segmentation and reconstruction qualitative results with other methods。

recommend:This article is obtained. CVPR 2020 Best Student Parassment Award。

paper 5:Generative Pretraining from Pixels

Summary:recently,OpenAI Released a new study,Aim to explore training on images GPT-2 Performance and no supervision accuracy performance。Researchers said,BERT and GPT-2 Wait Transformer Model is unknown,This means they can be applied directly in any form. 1D sequence。OpenAI Researchers training on images GPT-2(These images are decomposed into a pixel sequence),They call this model called iGPT。As a result, this model seems to be able to understand the appearance and category of objects, etc. 2D Image characteristics。iGPT The various consistency image samples generated can prove this,Even without people guidance。

iGPT Why can you succeed??This is because,Predict in next pixel(next pixel prediction)Significant enough transformer The model ultimately learns to generate a sample with clear identifiable objects。Once you have learned to generate such samples,Then「Synthetic analysis」,iGPT Will know the target category。Experiment,iGPT The characteristics of the model are realized on a large number of classified data sets. SOTA performance,In ImageNet The data set is close to SOTA Unison accuracy。

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iGPT Method flow chart。

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Researchers evaluate different models linear probe exist CIFAR-10、CIFAR-100 and STL-10 Performance on the data set,It is found that the method proposed in this study is superior to other supervision and no monitoring migration algorithms.。Even under full-tuning settings,iGPT Performance still has competitiveness。

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iGPT And current most self-supervised models linear probe Accuracy comparison。

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Researchers at low data CIFAR-10 Data set iGPT-L Conduct evaluation,As a result, it is found that simple image characteristics are simple. linear probe Excellent Mean Teacher and MixMatch,Weakly FixMatch。

recommend:Image field GPT The model is finally here.。

paper 6:ActBERT: Learning Global-Local Video-Text Representations

Summary:in the text,Researchers from Baidu Research Institute and Sydney University of Science and Technology ActBERT Mining global and local visual clues and text descriptions from a pair of video sequences,It uses rich context information and fine-grained relationship - Text joint modeling,Its contribution has the following three points:

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ActBERT Framework。

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YouCook2 Video description results on data set。

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COIN Action segmentation result on the data set。

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CrossTask Action step positioning result on the data set。

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Left:YouCook2 and MSR-VTT Text on the data set - Video segment retrieval result;right:MSR-VTT Video question and answer on the data set(multiple choice)result。


recommend:The frame is refreshed in five items SOTA,Fully demonstrate its learning capabilities in video text。

paper 7:A Survey on Dynamic Network Embedding

Summary:in the text,Researchers from Xi'an University of Electronic Science and Technology conducted systematic research on dynamic network embedding issues,Here, focus on the basic concepts of dynamic network embedding,And for the first time, the existing dynamic network embedding technology is classified.,Including matrix decomposition、Jumping model based model、Based on self-programming, embedding method based on neural network

also,Researchers summarize some common data sets and dynamic network embedding can play a positive role。on the basis of,They put forward some challenges facing existing algorithms,And suits the development direction that may promote future research,Dynamic embedding model、Large-scale dynamic network、Heterogeneous dynamics

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Dynamic network embedded by two parts,Embedded models and pretreatment flows, respectively。

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Dynamic network embedded feature value due to factor decomposition。

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Data sets to perform various network excavation tasks in dynamic network embedded experiments。

recommend:The highlight of this article is,Researchers put forward 6 Interesting and promising future research direction。

ArXiv Weekly Radiostation

The heart of the machine is combined by Chu Air、Luo RuotianArXiv Weekly Radiostation,exist 7 Papers On the basis of,Featured this week more important papers,includeNLP、CV、MLField10Selection,Details are as follows:

This week 10 Articles NLP Featured papers are:

1. Octet: Online Catalog Taxonomy Enrichment with Self-Supervision. (from Yuning Mao, Tong Zhao, Andrey Kan, Chenwei Zhang, Xin Luna Dong, Christos Faloutsos, Jiawei Han)

2. On the Learnability of Concepts: With Applications to Comparing Word Embedding Algorithms. (from Adam Sutton, Nello Cristianini)

3. The Role of Verb Semantics in Hungarian Verb-Object Order. (from Dorottya Demszky, László Kálmán, Dan Jurafsky, Beth Levin)

4. Building Low-Resource NER Models Using Non-Speaker Annotation. (from Tatiana Tsygankova, Francesca Marini, Stephen Mayhew, Dan Roth)

5. Multi-branch Attentive Transformer. (from Yang Fan, Shufang Xie, Yingce Xia, Lijun Wu, Tao Qin, Xiang-Yang Li, Tie-Yan Liu)

(bsp brass compression fittings to 1 4 tube)6. Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation. (from Jungo Kasai, Nikolaos Pappas, Hao Peng, James Cross, Noah A. Smith)

7. AMALGUM -- A Free, Balanced, Multilayer English Web Corpus. (from Luke Gessler, Siyao Peng, Yang Liu, Yilun Zhu, Shabnam Behzad, Amir Zeldes)

8. Communicative need modulates competition in language change. (from Andres Karjus, Richard A. Blythe, Simon Kirby, Kenny Smith)

9. Modeling Graph Structure via Relative Position for Better Text Generation from Knowledge Graphs. (from Martin Schmitt, Leonardo F. R. Ribeiro, Philipp Dufter, Iryna Gurevych, Hinrich Schütze)

10. How to Probe Sentence Embeddings in Low-Resource Languages: On Structural Design Choices for Probing Task Evaluation. (from Steffen Eger, Johannes Daxenberger, Iryna Gurevych)

(bsp brass compression fittings to 1 4 tube)This week 10 Articles CV Featured papers are:

1. LSD-C: Linearly Separable Deep Clusters. (from Sylvestre-Alvise Rebuffi, Sebastien Ehrhardt, Kai Han, Andrea Vedaldi, Andrew Zisserman)

2. Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks. (from He Wang, Zetian Jiang, Li Yi, Kaichun Mo, Hao Su, Leonidas J. Guibas)

(bsp brass compression fittings to 1 4 tube)3. Diverse Image Generation via Self-Conditioned GANs. (from Steven Liu, Tongzhou Wang, David Bau, Jun-Yan Zhu, Antonio Torralba)

(bsp brass compression fittings to 1 4 tube)4. AVLnet: Learning Audio-Visual Language Representations from Instructional Videos. (from Andrew Rouditchenko, Angie Boggust, David Harwath, Dhiraj Joshi, Samuel Thomas, Kartik Audhkhasi, Rogerio Feris, Brian Kingsbury, Michael Picheny, Antonio Torralba, James Glass)

5. Self-supervised Knowledge Distillation for Few-shot Learning. (from Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Mubarak Shah)

(bsp brass compression fittings to 1 4 tube)6. Learning Visual Commonsense for Robust Scene Graph Generation. (from Alireza Zareian, Haoxuan You, Zhecan Wang, Shih-Fu Chang)

7. Branch-Cooperative OSNet for Person Re-Identification. (from Lei Zhang, Xiaofu Wu, Suofei Zhang, Zirui Yin)

8. Rethinking Pre-training and Self-training. (from Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin D. Cubuk, Quoc V. Le)

9. Progressive Skeletonization: Trimming more fat from a network at initialization. (from Pau de Jorge, Amartya Sanyal, Harkirat S. Behl, Philip H.S. Torr, Gregory Rogez, Puneet K. Dokania)

(bsp brass compression fittings to 1 4 tube)10. Neural Graphics Pipeline for Controllable Image Generation. (from Xuelin Chen, Daniel Cohen-Or, Baoquan Chen, Niloy J. Mitra)

This week 10 Articles ML Featured papers are:

(bsp brass compression fittings to 1 4 tube)1. Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. (from Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera)

2. A Study of Compositional Generalization in Neural Models. (from Tim Klinger, Dhaval Adjodah, Vincent Marois, Josh Joseph, Matthew Riemer, Alex 'Sandy' Pentland, Murray Campbell)

3. Bandit-PAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits. (from Mo Tiwari, Martin Jinye Zhang, James Mayclin, Sebastian Thrun, Chris Piech, Ilan Shomorony)

4. Structured and Localized Image Restoration. (from Thomas Eboli, Alex Nowak-Vila, Jian Sun, Francis Bach, Jean Ponce, Alessandro Rudi)

5. Measuring Model Complexity of Neural Networks with Curve Activation Functions. (from Xia Hu, Weiqing Liu, Jiang Bian, Jian Pei)

6. Fully Test-time Adaptation by Entropy Minimization. (from Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, Trevor Darrell)

7. Learning to Track Dynamic Targets in Partially Known Environments. (from Heejin Jeong, Hamed Hassani, Manfred Morari, Daniel D. Lee, George J. Pappas)

8. Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets. (from Yanjie Dong, Georgios B. Giannakis, Tianyi Chen, Julian Cheng, Md. Jahangir Hossain, Victor C. M. Leung)

9. The Clever Hans Effect in Anomaly Detection. (from Jacob Kauffmann, Lukas Ruff, Grégoire Montavon, Klaus-Robert Müller)

10. How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks. (from Kirill Bykov, Marina M.-C. Höhne, Klaus-Robert Müller, Shinichi Nakajima, Marius Kloft)

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