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Paper Review

Paper Review #13 - InterpretML: A Unified Framework for Machine Learning Interpretability
Paper Review 2021. 9. 2. Paper Review #13 - InterpretML: A Unified Framework for Machine Learning Interpretability InterpretML: A Unified Framework for Machine Learning Interpretability (2019.09.19) https://arxiv.org/abs/1909.09223 InterpretML: A Unified Framework for Machine Learning Interpretability InterpretML is an open-source Python package which exposes machine learning interpretability algorithms to practitioners and researchers. InterpretML exposes two types of interpretability - glassbox models, whi..
Paper Review #12 - High-Performance Large-Scale Image Recognition Without Normalization
Paper Review 2021. 8. 26. Paper Review #12 - High-Performance Large-Scale Image Recognition Without Normalization High-Performance Large-Scale Image Recognition Without Normalization (2021.02.11) https://arxiv.org/abs/2102.06171 High-Performance Large-Scale Image Recognition Without Normalization Batch normalization is a key component of most image classification models, but it has many undesirable properties stemming from its dependence on the batch size and interactions between examples. Although recent w..
Paper Review #11 - Siamese Neural Networks for One-shot Image Recognition
Paper Review 2021. 8. 18. Paper Review #11 - Siamese Neural Networks for One-shot Image Recognition Siamese Neural Networks for One-shot Image Recognition (2015) https://www.semanticscholar.org/paper/Siamese-Neural-Networks-for-One-Shot-Image-Koch/f216444d4f2959b4520c61d20003fa30a199670a [PDF] Siamese Neural Networks for One-Shot Image Recognition | Semantic Scholar The process of learning good features for machine learning applications can be very computationally expensive and may prove diffi..
Paper Review #10 - Image Style Transfer Using Convolutional Neural Networks
Paper Review 2021. 8. 13. Paper Review #10 - Image Style Transfer Using Convolutional Neural Networks Image Style Transfer Using Convolutional Neural Networks (2016.06) https://ieeexplore.ieee.org/document/7780634 Image Style Transfer Using Convolutional Neural Networks Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent s..
Paper Review #9 - Photo-Relistic Single Image Super-Resolution Using a Generative Adversarial Network
Paper Review 2021. 8. 3. Paper Review #9 - Photo-Relistic Single Image Super-Resolution Using a Generative Adversarial Network Photo-Relistic Single Image Super-Resolution Using a Generative Adversarial Network (2017.05.25) - https://arxiv.org/abs/1609.04802 Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Despite the breakthroughs in accuracy and speed of single image super-resolution using faster and deeper convolutional neural networks, one central problem remains largely unsolved:..
Paper Review #8 - Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Paper Review 2021. 7. 30. Paper Review #8 - Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Unsupervised Representation Learning withDeep Convolutional Generative Adversarial Networks (2016.01.07) - https://arxiv.org/abs/1511.06434 Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning ..
Paper Review #7 - Generative Adversarial Nets
Paper Review 2021. 7. 26. Paper Review #7 - Generative Adversarial Nets Generative Adversarial Nets (2014.06.10) - https://arxiv.org/abs/1406.2661 Generative Adversarial Networks We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that arxiv.org Research direction & Moti..
Paper Review #6 - Understanding Back-Translation at Scale
Paper Review 2021. 7. 17. Paper Review #6 - Understanding Back-Translation at Scale Understanding Back-Translation at Scale (2018.10.03) - https://arxiv.org/abs/1808.09381 Understanding Back-Translation at Scale An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. This work broadens the understanding of back-translation and investigates a numb arxiv.org Res..

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