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Found 19 papers, 5 papers with code
Music Consistency Models
Consistency models have exhibited remarkable capabilities in facilitating efficient image/video generation, enabling synthesis with minimal sampling steps.
Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models
Transformers have catalyzed advancements in computer vision and natural language processing (NLP) fields.
Scalable Diffusion Models with State Space Backbone
We endeavor to train diffusion models for image data, wherein the traditional U-Net backbone is supplanted by a state space backbone, functioning on raw patches or latent space.
Tuning-Free Inversion-Enhanced Control for Consistent Image Editing
Consistent editing of real images is a challenging task, as it requires performing non-rigid edits (e. g., changing postures) to the main objects in the input image without changing their identity or attributes.
A-JEPA: Joint-Embedding Predictive Architecture Can Listen
The target representations of those regions are extracted by the exponential moving average of context encoder, \emph{i. e.}, target encoder, on the whole spectrogram.
Flatness-aware Adversarial Attack
We reveal that input regularization based methods make resultant adversarial examples biased towards flat extreme regions.
On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlook
Diffusion models and large language models have emerged as leading-edge generative models, revolutionizing various aspects of human life.
On the Robustness of Split Learning against Adversarial Attacks
Split learning enables collaborative deep learning model training while preserving data privacy and model security by avoiding direct sharing of raw data and model details (i. e., sever and clients only hold partial sub-networks and exchange intermediate computations).
Gradient-Free Textual Inversion
Recent works on personalized text-to-image generation usually learn to bind a special token with specific subjects or styles of a few given images by tuning its embedding through gradient descent.
Masked Auto-Encoders Meet Generative Adversarial Networks and Beyond
In this paper, we introduce a novel Generative Adversarial Networks alike framework, referred to as GAN-MAE, where a generator is used to generate the masked patches according to the remaining visible patches, and a discriminator is employed to predict whether the patch is synthesized by the generator.
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
Recent works have brought attention to the vulnerability of Federated Learning (FL) systems to gradient leakage attacks.
Uncertainty-Aware Image Captioning
It is well believed that the higher uncertainty in a word of the caption, the more inter-correlated context information is required to determine it.
Progressive Text-to-Image Generation
Recently, Vector Quantized AutoRegressive (VQ-AR) models have shown remarkable results in text-to-image synthesis by equally predicting discrete image tokens from the top left to bottom right in the latent space.
MaskBlock: Transferable Adversarial Examples with Bayes Approach
By contrast, we re-formulate crafting transferable AEs as the maximizing a posteriori probability estimation problem, which is an effective approach to boost the generalization of results with limited available data.
Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons
The opacity of neural networks leads their vulnerability to backdoor attacks, where hidden attention of infected neurons is triggered to override normal predictions to the attacker-chosen ones.
Case-Aware Adversarial Training
Specifically, the intuition stems from the fact that a very limited part of informative samples can contribute to most of model performance.
Enhance transferability of adversarial examples with model architecture
Transferability of adversarial examples is of critical importance to launch black-box adversarial attacks, where attackers are only allowed to access the output of the target model.
Backdoor Defense with Machine Unlearning
First, trigger pattern recovery is conducted to extract the trigger patterns infected by the victim model.
Rethinking BiSeNet For Real-time Semantic Segmentation
BiSeNet has been proved to be a popular two-stream network for real-time segmentation.
Ranked #8 on
Real-Time Semantic Segmentation
on Cityscapes test
