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Found 7 papers, 4 papers with code
What do Vision Transformers Learn? A Visual Exploration
In addition, we show that ViTs maintain spatial information in all layers except the final layer.
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.
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations
Existing techniques for model inversion typically rely on hard-to-tune regularizers, such as total variation or feature regularization, which must be individually calibrated for each network in order to produce adequate images.
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
A central tenet of Federated learning (FL), which trains models without centralizing user data, is privacy.
Ensemble Distillation for Structured Prediction: Calibrated, Accurate, Fast-Choose Three
However, extending these methods to structured prediction is not always straightforward or effective; furthermore, a held-out calibration set may not always be available.
Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
In doing so, we introduce and verify several hypotheses for why meta-learned models perform better.
WITCHcraft: Efficient PGD attacks with random step size
State-of-the-art adversarial attacks on neural networks use expensive iterative methods and numerous random restarts from different initial points.
