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Found 29 papers, 20 papers with code
Efficient Video Diffusion Models via Content-Frame Motion-Latent Decomposition
To tackle this issue, we propose content-motion latent diffusion model (CMD), a novel efficient extension of pretrained image diffusion models for video generation.
T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching
Sampling from diffusion probabilistic models (DPMs) is often expensive for high-quality image generation and typically requires many steps with a large model.
Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled
Deep generative models (DGMs) have been widely developed for graph data.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
Improving Generative Model-based Unfolding with Schrödinger Bridges
Machine learning-based unfolding has enabled unbinned and high-dimensional differential cross section measurements.
Fast Training of Diffusion Models with Masked Transformers
For masked training, we introduce an asymmetric encoder-decoder architecture consisting of a transformer encoder that operates only on unmasked patches and a lightweight transformer decoder on full patches.
Defending against Adversarial Audio via Diffusion Model
In this paper, we propose an adversarial purification-based defense pipeline, AudioPure, for acoustic systems via off-the-shelf diffusion models.
I$^2$SB: Image-to-Image Schrödinger Bridge
We propose Image-to-Image Schr\"odinger Bridge (I$^2$SB), a new class of conditional diffusion models that directly learn the nonlinear diffusion processes between two given distributions.
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning
Augmenting pretrained language models (LMs) with a vision encoder (e. g., Flamingo) has obtained the state-of-the-art results in image-to-text generation.
A Text-guided Protein Design Framework
Current AI-assisted protein design mainly utilizes protein sequential and structural information.
Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
Here we present a multi-modal molecule structure-text model, MoleculeSTM, by jointly learning molecules' chemical structures and textual descriptions via a contrastive learning strategy.
Fast Sampling of Diffusion Models via Operator Learning
Diffusion models have found widespread adoption in various areas.
DensePure: Understanding Diffusion Models towards Adversarial Robustness
By using the highest density point in the conditional distribution as the reversed sample, we identify the robust region of a given instance under the diffusion model's reverse process.
State-specific protein-ligand complex structure prediction with a multi-scale deep generative model
The binding complexes formed by proteins and small molecule ligands are ubiquitous and critical to life.
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models
In evaluating cross-dataset generalization with unseen categories, TPT performs on par with the state-of-the-art approaches that use additional training data.
Retrieval-based Controllable Molecule Generation
On various tasks ranging from simple design criteria to a challenging real-world scenario for designing lead compounds that bind to the SARS-CoV-2 main protease, we demonstrate our approach extrapolates well beyond the retrieval database, and achieves better performance and wider applicability than previous methods.
PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition
3D Point cloud is becoming a critical data representation in many real-world applications like autonomous driving, robotics, and medical imaging.
Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions
A significant gap remains between today's visual pattern recognition models and human-level visual cognition especially when it comes to few-shot learning and compositional reasoning of novel concepts.
Ranked #1 on
Few-Shot Image Classification
on Bongard-HOI
Diffusion Models for Adversarial Purification
Adversarial purification refers to a class of defense methods that remove adversarial perturbations using a generative model.
RelViT: Concept-guided Vision Transformer for Visual Relational Reasoning
This task remains challenging for current deep learning algorithms since it requires addressing three key technical problems jointly: 1) identifying object entities and their properties, 2) inferring semantic relations between pairs of entities, and 3) generalizing to novel object-relation combinations, i. e., systematic generalization.
Ranked #1 on
Zero-Shot Human-Object Interaction Detection
on HICO
Controllable and Compositional Generation with Latent-Space Energy-Based Models
In compositional generation, our method excels at zero-shot generation of unseen attribute combinations.
A Step-Wise Weighting Approach for Controllable Text Generation
Many existing approaches either require extensive training/fine-tuning of the LM for each single attribute under control or are slow to generate text.
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning
Inspired by the original one hundred BPs, we propose a new benchmark Bongard-LOGO for human-level concept learning and reasoning.
An Improved Semi-Supervised VAE for Learning Disentangled Representations
Learning interpretable and disentangled representations is a crucial yet challenging task in representation learning.
Semi-Supervised StyleGAN for Disentanglement Learning
Disentanglement learning is crucial for obtaining disentangled representations and controllable generation.
Disentangled GANs for Controllable Generation of High-Resolution Images
Generative adversarial networks (GANs) have achieved great success at generating realistic samples.
RelGAN: Relational Generative Adversarial Networks for Text Generation
Generative adversarial networks (GANs) have achieved great success at generating realistic images.
Ranked #3 on
Text Generation
on EMNLP2017 WMT
Towards a Better Understanding and Regularization of GAN Training Dynamics
Generative adversarial networks (GANs) are notoriously difficult to train and the reasons underlying their (non-)convergence behaviors are still not completely understood.
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations
Backpropagation-based visualizations have been proposed to interpret convolutional neural networks (CNNs), however a theory is missing to justify their behaviors: Guided backpropagation (GBP) and deconvolutional network (DeconvNet) generate more human-interpretable but less class-sensitive visualizations than saliency map.
