Search Results for author:
Found 22 papers, 18 papers with code
The Journey, Not the Destination: How Data Guides Diffusion Models
Then, we provide a method for computing these attributions efficiently.
OrthoNets: Orthogonal Channel Attention Networks
To circumvent this issue, FcaNet experimented on ImageNet to find optimal frequencies.
Rethinking Backdoor Attacks
In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation.
FFCV: Accelerating Training by Removing Data Bottlenecks
For example, we are able to train an ImageNet ResNet-50 model to 75\% in only 20 mins on a single machine.
Raising the Cost of Malicious AI-Powered Image Editing
We present an approach to mitigating the risks of malicious image editing posed by large diffusion models.
WaveNets: Wavelet Channel Attention Networks
Next, we test wavelet transform as a standalone channel compression method.
A Data-Based Perspective on Transfer Learning
It is commonly believed that in transfer learning including more pre-training data translates into better performance.
When does Bias Transfer in Transfer Learning?
Using transfer learning to adapt a pre-trained "source model" to a downstream "target task" can dramatically increase performance with seemingly no downside.
Missingness Bias in Model Debugging
Missingness, or the absence of features from an input, is a concept fundamental to many model debugging tools.
GHM Wavelet Transform for Deep Image Super Resolution
All single-level wavelets report similar results indicating that the convolutional neural network is invariant to choice of wavelet in a single-level filter approach.
Certified Patch Robustness via Smoothed Vision Transformers
Certified patch defenses can guarantee robustness of an image classifier to arbitrary changes within a bounded contiguous region.
CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning
The ability to perform causal and counterfactual reasoning are central properties of human intelligence.
3DB: A Framework for Debugging Computer Vision Models
We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation.
Unadversarial Examples: Designing Objects for Robust Vision
We study a class of realistic computer vision settings wherein one can influence the design of the objects being recognized.
Do Adversarially Robust ImageNet Models Transfer Better?
Typically, better pre-trained models yield better transfer results, suggesting that initial accuracy is a key aspect of transfer learning performance.
Improved Image Wasserstein Attacks and Defenses
Robustness against image perturbations bounded by a $\ell_p$ ball have been well-studied in recent literature.
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
We present a method for provably defending any pretrained image classifier against $\ell_p$ adversarial attacks.
Randomized Smoothing of All Shapes and Sizes
Randomized smoothing is the current state-of-the-art defense with provable robustness against $\ell_2$ adversarial attacks.
A Fine-Grained Spectral Perspective on Neural Networks
Are neural networks biased toward simple functions?
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
In this paper, we employ adversarial training to improve the performance of randomized smoothing.
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks
This framework works for neural networks with diverse architectures and nonlinearities and covers both primal and dual views of robustness verification.
Deep Diffeomorphic Normalizing Flows
We use a neural network to parametrize the smooth vector field and a recursive neural network (RNN) for approximating the solution of the ODE.
