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Found 14 papers, 12 papers with code
Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models
Our findings provide insights into the efficiency of the conditional denoising process that contribute to more practical and swift deployment of text-conditioned diffusion models.
Enhancing Neural Rendering Methods with Image Augmentations
We found that introducing image augmentations during training presents challenges such as geometric and photometric inconsistencies for learning NRMs from images.
Revisiting Test Time Adaptation under Online Evaluation
To address this issue, we propose a more realistic evaluation protocol for TTA methods, where data is received in an online fashion from a constant-speed data stream, thereby accounting for the method's adaptation speed.
Certified Robustness in Federated Learning
However, as in the single-node supervised learning setup, models trained in federated learning suffer from vulnerability to imperceptible input transformations known as adversarial attacks, questioning their deployment in security-related applications.
3DeformRS: Certifying Spatial Deformations on Point Clouds
In this work, we propose 3DeformRS, a method to certify the robustness of point cloud Deep Neural Networks (DNNs) against real-world deformations.
Towards Assessing and Characterizing the Semantic Robustness of Face Recognition
We propose a methodology for assessing and characterizing the robustness of FRMs against semantic perturbations to their input.
On the Robustness of Quality Measures for GANs
Finally, we show that the FID can be robustified by simply replacing the standard Inception with a robust Inception.
Generalized Real-World Super-Resolution through Adversarial Robustness
Instead of learning a dataset-specific degradation, we employ adversarial attacks to create difficult examples that target the model's weaknesses.
Enhancing Adversarial Robustness via Test-time Transformation Ensembling
Deep learning models are prone to being fooled by imperceptible perturbations known as adversarial attacks.
Towards Robust General Medical Image Segmentation
The reliability of Deep Learning systems depends on their accuracy but also on their robustness against adversarial perturbations to the input data.
Combating Adversaries with Anti-Adversaries
Deep neural networks are vulnerable to small input perturbations known as adversarial attacks.
Rethinking Clustering for Robustness
This paper studies how encouraging semantically-aligned features during deep neural network training can increase network robustness.
Gabor Layers Enhance Network Robustness
We revisit the benefits of merging classical vision concepts with deep learning models.
Dynamic Multimodal Instance Segmentation guided by natural language queries
We address the problem of segmenting an object given a natural language expression that describes it.
