7,536 research outputs found
SparseGAN: Sparse Generative Adversarial Network for Text Generation
It is still a challenging task to learn a neural text generation model under
the framework of generative adversarial networks (GANs) since the entire
training process is not differentiable. The existing training strategies either
suffer from unreliable gradient estimations or imprecise sentence
representations. Inspired by the principle of sparse coding, we propose a
SparseGAN that generates semantic-interpretable, but sparse sentence
representations as inputs to the discriminator. The key idea is that we treat
an embedding matrix as an over-complete dictionary, and use a linear
combination of very few selected word embeddings to approximate the output
feature representation of the generator at each time step. With such
semantic-rich representations, we not only reduce unnecessary noises for
efficient adversarial training, but also make the entire training process fully
differentiable. Experiments on multiple text generation datasets yield
performance improvements, especially in sequence-level metrics, such as BLEU
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts
We introduce an adversarial method for producing high-recall explanations of
neural text classifier decisions. Building on an existing architecture for
extractive explanations via hard attention, we add an adversarial layer which
scans the residual of the attention for remaining predictive signal. Motivated
by the important domain of detecting personal attacks in social media comments,
we additionally demonstrate the importance of manually setting a semantically
appropriate `default' behavior for the model by explicitly manipulating its
bias term. We develop a validation set of human-annotated personal attacks to
evaluate the impact of these changes.Comment: Accepted to EMNLP 2018 Code and data available at
https://github.com/shcarton/rcn
Channel-Recurrent Autoencoding for Image Modeling
Despite recent successes in synthesizing faces and bedrooms, existing
generative models struggle to capture more complex image types, potentially due
to the oversimplification of their latent space constructions. To tackle this
issue, building on Variational Autoencoders (VAEs), we integrate recurrent
connections across channels to both inference and generation steps, allowing
the high-level features to be captured in global-to-local, coarse-to-fine
manners. Combined with adversarial loss, our channel-recurrent VAE-GAN
(crVAE-GAN) outperforms VAE-GAN in generating a diverse spectrum of high
resolution images while maintaining the same level of computational efficacy.
Our model produces interpretable and expressive latent representations to
benefit downstream tasks such as image completion. Moreover, we propose two
novel regularizations, namely the KL objective weighting scheme over time steps
and mutual information maximization between transformed latent variables and
the outputs, to enhance the training.Comment: Code: https://github.com/WendyShang/crVAE. Supplementary Materials:
http://www-personal.umich.edu/~shangw/wacv18_supplementary_material.pd
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