Search Results for author:
Found 14 papers, 5 papers with code
Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks
Battle of the Backbones (BoB) makes this choice easier by benchmarking a diverse suite of pretrained models, including vision-language models, those trained via self-supervised learning, and the Stable Diffusion backbone, across a diverse set of computer vision tasks ranging from classification to object detection to OOD generalization and more.
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
Deep learning is increasingly moving towards a transfer learning paradigm whereby large foundation models are fine-tuned on downstream tasks, starting from an initialization learned on the source task.
Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch
In contrast, the Hidden Trigger Backdoor Attack achieves poisoning without placing a trigger into the training data at all.
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning
Federated learning is particularly susceptible to model poisoning and backdoor attacks because individual users have direct control over the training data and model updates.
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion
As a result, we may be able to craft more potent poisons by carefully choosing the base samples.
ATFaceGAN: Single Face Image Restoration and Recognition from Atmospheric Turbulence
To mitigate the degradation due to turbulence which includes deformation and blur, we propose a generative single frame restoration algorithm which disentangles the blur and deformation due to turbulence and reconstructs a restored image.
Towards Gender-Neutral Face Descriptors for Mitigating Bias in Face Recognition
Therefore, we present a novel `Adversarial Gender De-biasing algorithm (AGENDA)' to reduce the gender information present in face descriptors obtained from previously trained face recognition networks.
GANs with Variational Entropy Regularizers: Applications in Mitigating the Mode-Collapse Issue
To tackle this issue, we take an information-theoretic approach and maximize a variational lower bound on the entropy of the generated samples to increase their diversity.
Certified Watermarks for Neural Networks
Watermarking is a commonly used strategy to protect creators' rights to digital images, videos and audio.
Contrastive Learning is Just Meta-Learning
Contrastive learning has recently taken off as a paradigm for learning from unlabeled data.
Identification of Attack-Specific Signatures in Adversarial Examples
The adversarial attack literature contains a myriad of algorithms for crafting perturbations which yield pathological behavior in neural networks.
Mutual Adversarial Training: Learning together is better than going alone
In other words, we can make a weak model more robust with the help of a strong teacher model.
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses
Under JSTM, we develop novel adversarial attacks and defenses.
A Deep Dive into Dataset Imbalance and Bias in Face Identification
This is an unfortunate omission, as 'imbalance' is a more complex matter in identification; imbalance may arise in not only the training data, but also the testing data, and furthermore may affect the proportion of identities belonging to each demographic group or the number of images belonging to each identity.
