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Found 16 papers, 10 papers with code
MetaCloak: Preventing Unauthorized Subject-driven Text-to-image Diffusion-based Synthesis via Meta-learning
To solve these challenges, we propose MetaCloak, which solves the bi-level poisoning problem with a meta-learning framework with an additional transformation sampling process to craft transferable and robust perturbation.
Joint Demosaicing and Denoising with Double Deep Image Priors
Demosaicing and denoising of RAW images are crucial steps in the processing pipeline of modern digital cameras.
Memory-adaptive Depth-wise Heterogenous Federated Learning
Federated learning is a promising paradigm that allows multiple clients to collaboratively train a model without sharing the local data.
A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT
The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.
Infusing Definiteness into Randomness: Rethinking Composition Styles for Deep Image Matting
Inspired by this, we introduce a novel composition style that binds the source and combined foregrounds in a definite triplet.
Tackling Data Heterogeneity in Federated Learning with Class Prototypes
We propose FedNH, a novel method that improves the local models' performance for both personalization and generalization by combining the uniformity and semantics of class prototypes.
iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations
In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only.
Boosting Robustness of Image Matting with Context Assembling and Strong Data Augmentation
Deep image matting methods have achieved increasingly better results on benchmarks (e. g., Composition-1k/alphamatting. com).
FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization
To this end, this paper proposes FedHM, a novel heterogeneous federated model compression framework, distributing the heterogeneous low-rank models to clients and then aggregating them into a full-rank model.
FedDiscrete: A Secure Federated Learning Algorithm Against Weight Poisoning
Federated learning (FL) is a privacy-aware collaborative learning paradigm that allows multiple parties to jointly train a machine learning model without sharing their private data.
Local-Global Knowledge Distillation in Heterogeneous Federated Learning with Non-IID Data
Federated learning enables multiple clients to collaboratively learn a global model by periodically aggregating the clients' models without transferring the local data.
Learning Affinity-Aware Upsampling for Deep Image Matting
By looking at existing upsampling operators from a unified mathematical perspective, we generalize them into a second-order form and introduce Affinity-Aware Upsampling (A2U) where upsampling kernels are generated using a light-weight lowrank bilinear model and are conditioned on second-order features.
A Subspace Acceleration Method for Minimization Involving a Group Sparsity-Inducing Regularizer
We consider the problem of minimizing an objective function that is the sum of a convex function and a group sparsity-inducing regularizer.
Optimization and Control 49M37, 65K05, 65K10, 65Y20, 68Q25, 90C30, 90C60
In defense of OSVOS
Through adding a common module, video loss, which we formulate with various forms of constraints (including weighted BCE loss, high-dimensional triplet loss, as well as a novel mixed instance-aware video loss), to train the parent network in the step (2), the network is then better prepared for the step (3), i. e. online fine-tuning on the target instance.
Index Network
By viewing the indices as a function of the feature map, we introduce the concept of "learning to index", and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the downsampling and upsampling stages, without extra training supervision.
Ranked #2 on
Grayscale Image Denoising
on Set12 sigma30
Indices Matter: Learning to Index for Deep Image Matting
We show that existing upsampling operators can be unified with the notion of the index function.
