GENERATIVE ADVERSARIAL NETS

By Individualized Treat, Jinsung Yoon. 2018

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In Generative Models

Citations in the corpus

MaskGAN: Better Text Generation via Filling in the ______ (2018, background): “Generative Adversarial Networks (GANs) (Goodfellow et al., 2014) are a framework for training generative models in an adversarial setup, with a generator generating images that is trying to fool a discriminator that is trained to discriminate between real and synthetic images.”
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets (2016, methodology): “[4] introduced the Generative Adversarial Networks (GAN), a framework for training deep generative models using a minimax game.”
Denoising Diffusion Probabilistic Models (2020, background): “…adversarial networks (GANs), autoregressive models, ﬂows, and variational autoencoders (VAEs) have synthesized striking image and audio samples [12, 25, 3, 55, 35, 23, 10, 30, 41, 54, 24, 31, 42], and there have been remarkable advances in energy-based modeling and score matching that have…”
A Simple Framework for Contrastive Learning of Visual Representations (2020, neutral): “Generative approaches learn to generate or otherwise model pixels in the input space (Hinton et al., 2006; Kingma & Welling, 2013; Goodfellow et al., 2014).”
Momentum Contrast for Unsupervised Visual Representation Learning (2019, background): “Adversarial losses [24] measure the difference between probability distributions.”
CTRL: A Conditional Transformer Language Model for Controllable Generation (2019, background): “In computer vision, the advent of generative adversarial networks (Goodfellow et al., 2014) improved image generation.”
Music Transformer: Generating Music with Long-Term Structure (2018, background): “Pianorolls are also image-like and can be modeled by CNNs trained either as generative adversarial networks (GAN; Goodfellow et al., 2014).”
Neural Autoregressive Distribution Estimation (2016, background): “Goodfellow et al. (2014) also proposed an adversarial approach to training directed generative networks, that relies on a discriminator network simultaneously trained to distinguish between data and model samples.”
ColdGANs: Taming Language GANs with Cautious Sampling Strategies (2020, background): “A natural alternative, not requiring human judgments, is to frame the problem under the Generative Adversarial Network (GAN) paradigm [14], which has been used successfully for image generation [3].”
Residual Energy-Based Models for Text Generation (2020, background): “Generative Adversarial Networks (Goodfellow et al., 2014) also relate to EBMs, except that in EBMs the generator is implicit and negatives samples are produced by the discriminator itself.”
Discriminative Adversarial Search for Abstractive Summarization (2020, positive): “To tackle exposure bias, Generative Adversarial Networks (GANs) (Goodfellow et al., 2014) represent a natural alternative to the proposed approaches: rather than learning a speciﬁc metric, the model learns to generate text that a discriminator can not differentiate from human-produced content.”
Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering (2019, methodology): “(2018) followed the GAN (Goodfellow et al., 2014) paradigm, taking QG as a generator and QA as a discriminator, to utilize unlabeled data.”
Robust Neural Machine Translation with Joint Textual and Phonetic Embedding (2018, background): “…of such vulnerability are complicated, and some of them are: 1) neural networks are inherently sensitive to noises, such as adversarial examples (Goodfellow et al., 2014; Szegedy et al., 2013), 2) the global effect of attention, where every input word can affect every output word generated by…”
Object-Centric Learning with Slot Attention (2020, neutral): “[80] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio.”
Group Normalization (2018, methodology): “These methods are effective for training sequential models (RNN/LSTM [22,23]) or generative models (GANs [24,25]).”
Image Transformer (2018, background): “Another, popular direction of research in image generation is training models with an adversarial loss (Goodfellow et al., 2014).”
Big Self-Supervised Models are Strong Semi-Supervised Learners (2020, background): “These contrastive learning based approaches learn representations in a discriminative fashion instead of a generative one as in [3, 49, 50, 41, 51].”
Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (2020, background): “This is similar to GANs [66], where there is no loss that is jointly minimized w.”