From 84e8d27b6e2ed76c0744bedd3713dc7575c75843 Mon Sep 17 00:00:00 2001 From: Matthew Vowels Date: Mon, 7 Oct 2019 14:09:13 +0100 Subject: [PATCH] 277 --- .DS_Store | Bin 0 -> 6148 bytes .ipynb_checkpoints/delete-checkpoint.ipynb | 6 + .ipynb_checkpoints/ii_loss-checkpoint.ipynb | 131 ++++++++++++++++++++ README.md | 43 +++++-- VAE_literature.xlsx | Bin 120453 -> 131198 bytes ~$VAE_literature.xlsx | Bin 0 -> 165 bytes 6 files changed, 172 insertions(+), 8 deletions(-) create mode 100644 .DS_Store create mode 100644 .ipynb_checkpoints/delete-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/ii_loss-checkpoint.ipynb create mode 100644 ~$VAE_literature.xlsx diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..480a4baa4a375842b89d0e64ab16b00d1c6311cf GIT binary patch literal 6148 zcmeHKJxc>Y5PhqML~Q~hSYBaalZds=C0q(YB`DgeF+s(I3%QG?l570k`et?&VofzP-PP^zFT=Xc6l;LkkV0I3dma1qQ8a<K^*)OWDiuNN# zri}q(z!>;sK)w$#n_v>K^r)W>D!l>_Bbrs%*T4S6SPaA@VCj(-iu0*NpX$OD!-boJ z^Xc>_A-?qJ)8TYw&J$L4;fCVE#lyuK4wrZ|Z44L#(+sToVO7rm*3b9<=_DH&1IECg zV!%b|dD>)2;cT5LPR`nx?V3$Q;!2O2LKTi j])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "ii_loss = intra_spread - inter_separation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/README.md b/README.md index ed9be23..dbfa237 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,29 @@ # Awesome-VAEs Awesome work on the VAE, disentanglement, representation learning, and generative models. -I gathered these resources (currently @ 263 papers) as literature for my PhD, and thought it may come in useful for others. This list includes works relevant to various topics relating to VAEs. Sometimes this spills over to topics e.g. adversarial training and GANs, general disentanglement, variational inference, flow-based models and auto-regressive models. Always keen to expand the list. I have also included an excel file which includes notes on each paper, as well as a breakdown of the topics covered in each paper. +I gathered these resources (currently @ 277 papers) as literature for my PhD, and thought it may come in useful for others. This list includes works relevant to various topics relating to VAEs. Sometimes this spills over to topics e.g. adversarial training and GANs, general disentanglement, variational inference, flow-based models and auto-regressive models. Always keen to expand the list. I have also included an excel file which includes notes on each paper, as well as a breakdown of the topics covered in each paper. They are ordered by year (new to old). I provide a link to the paper as well as to the github repo where available. ## 2019 +Hamiltonian generative networks. Toth, Rezende, Jaegle, Racaniere, Botev, Higgins https://128.84.21.199/pdf/1909.13789.pdf + +LAVAE: Disentangling location and appearance. Dittadi, Winther https://arxiv.org/pdf/1909.11813.pdf + +Interpretable models in probabilistic machine learning. Kim https://ora.ox.ac.uk/objects/uuid:b238ed7d-7155-4860-960e-6227c7d688fb/download_file?file_format=pdf&safe_filename=PhD_Thesis_of_University_of_Oxford.pdf&type_of_work=Thesis + + +Disentangling speech and non-speech components for building robust acoustic models from found data. Gurunath, Rallabandi, Black https://arxiv.org/pdf/1909.11727.pdf + +Joint separation, dereverberation and classification of multiple sources using multichannel variational autoencoder with auxiliary classifier. Inoue, Kameoka, Li, Makino http://pub.dega-akustik.de/ICA2019/data/articles/000906.pdf + +SuperVAE: Superpixelwise variational autoencoder for salient object detection. Li, Sun, Guo https://www.aaai.org/ojs/index.php/AAAI/article/view/4876 + +Implicit discriminator in variational autoencoder. Munjal, Paul, Krishnan https://arxiv.org/pdf/1909.13062.pdf + +TransGaGa: Geometry-aware unsupervised image-to-image translation. Wu, Cao, Li, Qian, Loy http://openaccess.thecvf.com/content_CVPR_2019/papers/Wu_TransGaGa_Geometry-Aware_Unsupervised_Image-To-Image_Translation_CVPR_2019_paper.pdf + Variational attention using articulatory priors for generating code mixed speech using monolingual corpora. Rallabandi, Black. https://www.isca-speech.org/archive/Interspeech_2019/pdfs/1103.pdf One-class collaborative filtering with the queryable variational autoencoder. Wu, Bouadjenek, Sanner. https://people.eng.unimelb.edu.au/mbouadjenek/papers/SIGIR_Short_2019.pdf @@ -16,21 +33,21 @@ Predictive auxiliary variational autoencoder for representation learning of glob Data augmentation using variational autoencoder for embedding based speaker verification. Wu, Wang, Qian, Yu https://zhanghaowu.me/assets/VAE_Data_Augmentation_proceeding.pdf One-shot voice conversion with disentangled representations by leveraging phonetic posteriograms. Mohammadi, Kim. https://www.isca-speech.org/archive/Interspeech_2019/pdfs/1798.pdf - + EEG-based adaptive driver-vehicle interface using variational autoencoder and PI-TSVM. Bi, Zhang, Lian https://www.researchgate.net/profile/Luzheng_Bi2/publication/335619300_EEG-Based_Adaptive_Driver-Vehicle_Interface_Using_Variational_Autoencoder_and_PI-TSVM/links/5d70bb234585151ee49e5a30/EEG-Based-Adaptive-Driver-Vehicle-Interface-Using-Variational-Autoencoder-and-PI-TSVM.pdf - + Neural gaussian copula for variational autoencoder Wang, Wang https://arxiv.org/pdf/1909.03569.pdf - + Enhancing VAEs for collaborative filtering: Flexible priors and gating mechanisms. Kim, Suh http://delivery.acm.org/10.1145/3350000/3347015/p403-kim.pdf?ip=86.162.136.199&id=3347015&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1568726810_89cfa7cbc7c1b0663405d4446f9fce85 - + Riemannian normalizing flow on variational wasserstein autoencoder for text modeling. Wang, Wang https://arxiv.org/pdf/1904.02399.pdf Disentanglement with hyperspherical latent spaces using diffusion variational autoencoders. Rey https://openreview.net/pdf?id=SylFDSU6Sr Learning deep representations by mutual information estimation and maximization. Hjelm, Fedorov, Lavoie-Marchildon, Grewal, Bachman, Trischler, Bengio https://arxiv.org/pdf/1808.06670.pdf https://github.com/rdevon/DIM - + Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation. Vladymyrov, Ariga https://arxiv.org/pdf/1909.04427.pdf - + Real time trajectory prediction using conditional generative models. Gomez-Gonzalez, Prokudin, Scholkopf, Peters https://arxiv.org/pdf/1909.03895.pdf Disentanglement challenge: from regularization to reconstruction. Qiao, Li, Cai https://openreview.net/pdf?id=ByecPrUaHH @@ -203,6 +220,12 @@ Causal discovery with attention-based convolutional neural networks. Naura, Buc ## 2018 +Bias and generalization in deep generative models: an empirical study. Zhao, Ren, Yuan, Song, Goodman, Ermon https://arxiv.org/pdf/1811.03259.pdf https://ermongroup.github.io/blog/bias-and-generalization-dgm/ https://github.com/ermongroup/BiasAndGeneralization/tree/master/Evaluate + +On variational lower bounds of mutual information. Poole, Ozair, van den Oord, Alemi, Tucker http://bayesiandeeplearning.org/2018/papers/136.pdf + +GAN - why it is so hard to train generative adversarial networks . Hui https://medium.com/@jonathan_hui/gan-why-it-is-so-hard-to-train-generative-advisory-networks-819a86b3750b + Counterfactuals uncover the modular structure of deep generative models. Besserve, Sun, Scholkopf. https://arxiv.org/pdf/1812.03253.pdf Learning independent causal mechanisms. Parascandolo, Kilbertus, Rojas-Carulla, Scholkopf https://arxiv.org/pdf/1712.00961.pdf @@ -351,7 +374,7 @@ Mutual information neural estimation. Belghazi, Baratin, Rajeswar, Ozair, Bengio Explorations in homeomorphic variational auto-encoding. Falorsi, de Haan, Davidson, Cao, Weiler, Forre, Cohen. https://arxiv.org/pdf/1807.04689.pdf https://github.com/pimdh/lie-vae -On variational lower bounds of mutual information. Poole, Ozair, van den Oord, Alemi, Tucker http://bayesiandeeplearning.org/2018/papers/136.pdf +Hierarchical variational memory network for dialogue generation. Chen, Ren, Tang, Zhao, Yin http://delivery.acm.org/10.1145/3190000/3186077/p1653-chen.pdf?ip=86.162.136.199&id=3186077&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1569938843_c07ad21d173fc64a44a22fd6521140cb ## 2017 @@ -442,9 +465,13 @@ VEEGAN: Reducing mode collapse in GANs using implicit variational learning. Sriv Discovering discrete latent topics with neural variational inference. Miao, Grefenstette, Blunsom https://arxiv.org/pdf/1706.00359.pdf +Variational approaches for auto-encoding generative adversarial networks. Rosca, Lakshminarayana, Warde-Farley, Mohamed https://arxiv.org/pdf/1706.04987.pdf + ## 2016 +Deep feature consistent variational autoencoder. Hou, Shen, Sun, Qiu https://arxiv.org/pdf/1610.00291.pdf https://github.com/sbavon/Deep-Feature-Consistent-Variational-AutoEncoder-in-Tensorflow + Neural variational inference for text processing. Miao, Yu, Grefenstette, Blunsom. https://arxiv.org/pdf/1511.06038.pdf Domain-adversarial training of neural networks. 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