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Found 8 papers, 6 papers with code
WCP: Worst-Case Perturbations for Semi-Supervised Deep Learning
The WCP can be minimized on both labeled and unlabeled data so that networks can be trained in a semi-supervised fashion.
Learning Generalized Transformation Equivariant Representations via Autoencoding Transformations
Transformation Equivariant Representations (TERs) aim to capture the intrinsic visual structures that equivary to various transformations by expanding the notion of {\em translation} equivariance underlying the success of Convolutional Neural Networks (CNNs).
AVT: Unsupervised Learning of Transformation Equivariant Representations by Autoencoding Variational Transformations
This ensures the resultant TERs of individual images contain the {\em intrinsic} information about their visual structures that would equivary {\em extricably} under various transformations in a generalized {\em nonlinear} case.
AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data
The success of deep neural networks often relies on a large amount of labeled examples, which can be difficult to obtain in many real scenarios.
Gated Context Aggregation Network for Image Dehazing and Deraining
Image dehazing aims to recover the uncorrupted content from a hazy image.
Ranked #1 on
Rain Removal
on DID-MDN
CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces
In this paper, we formalize the idea behind capsule nets of using a capsule vector rather than a neuron activation to predict the label of samples.
Global versus Localized Generative Adversarial Nets
In this paper, we present a novel localized Generative Adversarial Net (GAN) to learn on the manifold of real data.
Stock Price Prediction via Discovering Multi-Frequency Trading Patterns
Then the future stock prices are predicted as a nonlinear mapping of the combination of these components in an Inverse Fourier Transform (IFT) fashion.
