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Found 17 papers, 9 papers with code
GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers
As transformers are equivariant to the permutation of input tokens, encoding the positional information of tokens is necessary for many tasks.
Neural Fourier Transform: A General Approach to Equivariant Representation Learning
Symmetry learning has proven to be an effective approach for extracting the hidden structure of data, with the concept of equivariance relation playing the central role.
Invariance-adapted decomposition and Lasso-type contrastive learning
Recent years have witnessed the effectiveness of contrastive learning in obtaining the representation of dataset that is useful in interpretation and downstream tasks.
Unsupervised Learning of Equivariant Structure from Sequences
In this study, we present meta-sequential prediction (MSP), an unsupervised framework to learn the symmetry from the time sequence of length at least three.
Contrastive Representation Learning with Trainable Augmentation Channel
In contrastive representation learning, data representation is trained so that it can classify the image instances even when the images are altered by augmentations.
Robustness to Adversarial Perturbations in Learning from Incomplete Data
What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed?
Adaptive Sample-space & Adaptive Probability coding: a neural-network based approach for compression
We propose Adaptive Sample-space & Adaptive Probability (ASAP) coding, an efficient neural-network based method for lossy data compression.
Spatially Controllable Image Synthesis with Internal Representation Collaging
We present a novel CNN-based image editing strategy that allows the user to change the semantic information of an image over an arbitrary region by manipulating the feature-space representation of the image in a trained GAN model.
Neural Multi-scale Image Compression
This study presents a new lossy image compression method that utilizes the multi-scale features of natural images.
Spectral Normalization for Generative Adversarial Networks
One of the challenges in the study of generative adversarial networks is the instability of its training.
Ranked #20 on
Image Generation
on STL-10
cGANs with Projection Discriminator
We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model.
Ranked #14 on
Conditional Image Generation
on CIFAR-10
Parameter Reference Loss for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to utilize labeled data from a source domain to learn a model that generalizes to a target domain of unlabeled data.
Spectral Norm Regularization for Improving the Generalizability of Deep Learning
We investigate the generalizability of deep learning based on the sensitivity to input perturbation.
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets.
Learning Discrete Representations via Information Maximizing Self-Augmented Training
Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation.
Ranked #3 on
Unsupervised Image Classification
on SVHN
(using extra training data)
Adversarial Training Methods for Semi-Supervised Text Classification
We extend adversarial and virtual adversarial training to the text domain by applying perturbations to the word embeddings in a recurrent neural network rather than to the original input itself.
Ranked #21 on
Sentiment Analysis
on IMDb
General Classification
Semi-Supervised Text Classification
+2
Distributional Smoothing with Virtual Adversarial Training
We propose local distributional smoothness (LDS), a new notion of smoothness for statistical model that can be used as a regularization term to promote the smoothness of the model distribution.
