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Found 10 papers, 9 papers with code
M-EBM: Towards Understanding the Manifolds of Energy-Based Models
Despite its simplicity, M-EBM significantly improves unconditional EBMs in training stability and speed on a host of benchmark datasets, such as CIFAR10, CIFAR100, CelebA-HQ, and ImageNet 32x32.
APSNet: Attention Based Point Cloud Sampling
Traditional task-agnostic sampling methods, such as farthest point sampling (FPS), do not consider downstream tasks when sampling point clouds, and thus non-informative points to the tasks are often sampled.
Towards Bridging the Performance Gaps of Joint Energy-based Models
This question has recently been answered in the affirmative, introducing the field of Joint Energy-based Model (JEM), which achieves high classification accuracy and image generation quality simultaneously.
Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Model
Diffusion Denoising Probability Models (DDPM) and Vision Transformer (ViT) have demonstrated significant progress in generative tasks and discriminative tasks, respectively, and thus far these models have largely been developed in their own domains.
A Hierarchical N-Gram Framework for Zero-Shot Link Prediction
Thus, in this paper, we propose a Hierarchical N-Gram framework for Zero-Shot Link Prediction (HNZSLP), which considers the dependencies among character n-grams of the relation surface name for ZSLP.
JEM++: Improved Techniques for Training JEM
1) We propose a proximal SGLD to generate samples in the proximity of samples from the previous step, which improves the stability.
Improving Text-to-Image Synthesis Using Contrastive Learning
To address this issue, we propose a contrastive learning approach to improve the quality and enhance the semantic consistency of synthetic images.
Ranked #9 on
Text-to-Image Generation
on CUB
Generative Max-Mahalanobis Classifiers for Image Classification, Generation and More
We show that our Generative MMC (GMMC) can be trained discriminatively, generatively, or jointly for image classification and generation.
A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention
The success of Deep Neural Networks (DNNs) highly depends on data quality.
Learning with Multiplicative Perturbations
In this paper, we propose xAT and xVAT, new adversarial training algorithms, that generate \textbf{multiplicative} perturbations to input examples for robust training of DNNs.
