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Found 7 papers, 1 papers with code
An Intermediate Fusion ViT Enables Efficient Text-Image Alignment in Diffusion Models
Diffusion models have been widely used for conditional data cross-modal generation tasks such as text-to-image and text-to-video.
Efficient Multimodal Diffusion Models Using Joint Data Infilling with Partially Shared U-Net
Recently, diffusion models have been used successfully to fit distributions for cross-modal data translation and multimodal data generation.
Class-Incremental Learning Using Generative Experience Replay Based on Time-aware Regularization
Learning new tasks accumulatively without forgetting remains a critical challenge in continual learning.
Cognitively Inspired Cross-Modal Data Generation Using Diffusion Models
Most existing cross-modal generative methods based on diffusion models use guidance to provide control over the latent space to enable conditional generation across different modalities.
Encoding Binary Concepts in the Latent Space of Generative Models for Enhancing Data Representation
We propose a novel binarized regularization to facilitate learning of binary concepts to improve the quality of data generation in autoencoders.
Evaluating NLP Systems On a Novel Cloze Task: Judging the Plausibility of Possible Fillers in Instructional Texts
Cloze task is a widely used task to evaluate an NLP system's language understanding ability.
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL
Extending the successes in supervised learning methods to the reinforcement learning (RL) setting, however, is difficult due to the data generating process - RL agents actively query their environment for data, and the data are a function of the policy followed by the agent.
Out of Distribution (OOD) Detection
Reinforcement Learning (RL)
