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Found 15 papers, 4 papers with code
CPR: Retrieval Augmented Generation for Copyright Protection
To reduce risks of leaking private information contained in the retrieved set, we introduce Copy-Protected generation with Retrieval (CPR), a new method for RAG with strong copyright protection guarantees in a mixed-private setting for diffusion models. CPR allows to condition the output of diffusion models on a set of retrieved images, while also guaranteeing that unique identifiable information about those example is not exposed in the generated outputs.
Training Data Protection with Compositional Diffusion Models
We introduce Compartmentalized Diffusion Models (CDM), a method to train different diffusion models (or prompts) on distinct data sources and arbitrarily compose them at inference time.
Tangent Transformers for Composition, Privacy and Removal
We introduce Tangent Attention Fine-Tuning (TAFT), a method for fine-tuning linearized transformers obtained by computing a First-order Taylor Expansion around a pre-trained initialization.
SAFE: Machine Unlearning With Shard Graphs
We present Synergy Aware Forgetting Ensemble (SAFE), a method to adapt large models on a diverse collection of data while minimizing the expected cost to remove the influence of training samples from the trained model.
Integral Continual Learning Along the Tangent Vector Field of Tasks
We propose a lightweight continual learning method which incorporates information from specialized datasets incrementally, by integrating it along the vector field of "generalist" models.
On Leave-One-Out Conditional Mutual Information For Generalization
We derive information theoretic generalization bounds for supervised learning algorithms based on a new measure of leave-one-out conditional mutual information (loo-CMI).
Mixed Differential Privacy in Computer Vision
AdaMix incorporates few-shot training, or cross-modal zero-shot learning, on public data prior to private fine-tuning, to improve the trade-off.
Scene Uncertainty and the Wellington Posterior of Deterministic Image Classifiers
We propose a method to estimate the uncertainty of the outcome of an image classifier on a given input datum.
Mixed-Privacy Forgetting in Deep Networks
We show that the influence of a subset of the training samples can be removed -- or "forgotten" -- from the weights of a network trained on large-scale image classification tasks, and we provide strong computable bounds on the amount of remaining information after forgetting.
LQF: Linear Quadratic Fine-Tuning
Classifiers that are linear in their parameters, and trained by optimizing a convex loss function, have predictable behavior with respect to changes in the training data, initial conditions, and optimization.
Forgetting Outside the Box: Scrubbing Deep Networks of Information Accessible from Input-Output Observations
We describe a procedure for removing dependency on a cohort of training data from a trained deep network that improves upon and generalizes previous methods to different readout functions and can be extended to ensure forgetting in the activations of the network.
Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks
We explore the problem of selectively forgetting a particular subset of the data used for training a deep neural network.
Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence
Deep neural networks (DNNs), however, challenge this view: We show that removing regularization after an initial transient period has little effect on generalization, even if the final loss landscape is the same as if there had been no regularization.
Sparse Kernel PCA for Outlier Detection
In this paper, we propose a new method to perform Sparse Kernel Principal Component Analysis (SKPCA) and also mathematically analyze the validity of SKPCA.
Classification of Breast Cancer Histology using Deep Learning
In this paper, we propose a deep learning-based method for classification of H&E stained breast tissue images released for BACH challenge 2018 by fine-tuning Inception-v3 convolutional neural network (CNN) proposed by Szegedy et al.
