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Found 12 papers, 8 papers with code
Client-Centric Federated Adaptive Optimization
Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private.
Advancing Video Quality Assessment for AIGC
In recent years, AI generative models have made remarkable progress across various domains, including text generation, image generation, and video generation.
Revisiting Video Quality Assessment from the Perspective of Generalization
We begin by analyzing the weight loss landscape of VQA models, identifying a strong correlation between this landscape and the generalization gaps.
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration.
On the Role of Server Momentum in Federated Learning
To our best knowledge, this is the first work that thoroughly analyzes the performances of server momentum with a hyperparameter scheduler and system heterogeneity.
Solving a Class of Non-Convex Minimax Optimization in Federated Learning
We propose FL algorithms (FedSGDA+ and FedSGDA-M) and reduce existing complexity results for the most common minimax problems.
Enhance Diffusion to Improve Robust Generalization
We approximate the dynamic of PGD-AT by a continuous-time Stochastic Differential Equation (SDE), and show that the diffusion term of this SDE determines the robust generalization.
EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs
To answer it, we propose a generalization bound for dynamic non-IID transfer learning on graphs, which implies the generalization performance is dominated by domain evolution and domain discrepancy between source and target graphs.
Augmenting Knowledge Transfer across Graphs
Given a resource-rich source graph and a resource-scarce target graph, how can we effectively transfer knowledge across graphs and ensure a good generalization performance?
Understanding and Enhancing Robustness of Concept-based Models
Subsequently, we propose a potential general adversarial training-based defense mechanism to increase robustness of these systems to the proposed malicious attacks.
Correlation Networks for Extreme Multi-label Text Classification
This paper develops the Correlation Networks (CorNet) architecture for the extreme multi-label text classification (XMTC) task, where the objective is to tag an input text sequence with the most relevant subset of labels from an extremely large label set.
Multi Label Text Classification
Multi-Label Text Classification
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