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Found 7 papers, 2 papers with code
Text Style Transfer: Leveraging a Style Classifier on Entangled Latent Representations
We propose a novel text style transfer algorithm with entangled latent representation, and introduce a style classifier that can regulate the latent structure and transfer style.
$f$-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning
In self-supervised contrastive learning, a widely-adopted objective function is InfoNCE, which uses the heuristic cosine similarity for the representation comparison, and is closely related to maximizing the Kullback-Leibler (KL)-based mutual information.
SoftEdge: Regularizing Graph Classification with Random Soft Edges
Augmented graphs play a vital role in regularizing Graph Neural Networks (GNNs), which leverage information exchange along edges in graphs, in the form of message passing, for learning.
Revisiting flow generative models for Out-of-distribution detection
Deep generative models have been widely used in practical applications such as the detection of out-of-distribution (OOD) data.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
$f$-Mutual Information Contrastive Learning
Self-supervised contrastive learning is an emerging field due to its power in providing good data representations.
Symmetric Wasserstein Autoencoders
With the symmetric treatment of the data and the latent representation, the algorithm implicitly preserves the local structure of the data in the latent space.
Multivariate Triangular Quantile Maps for Novelty Detection
Novelty detection, a fundamental task in machine learning, has drawn a lot of recent attention due to its wide-ranging applications and the rise of neural approaches.
