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Found 11 papers, 2 papers with code
Exploring Dark Knowledge under Various Teacher Capacities and Addressing Capacity Mismatch
Two fundamental observations are: (1) a larger teacher tends to produce probability vectors that are less distinct between non-ground-truth classes; (2) teachers with different capacities are basically consistent in their cognition of relative class affinity.
Visualizing, Rethinking, and Mining the Loss Landscape of Deep Neural Networks
The loss landscape of deep neural networks (DNNs) is commonly considered complex and wildly fluctuated.
Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks
Exploring the loss landscape offers insights into the inherent principles of deep neural networks (DNNs).
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
For better model personalization, we point out that the hard-won personalized models are not well exploited and propose "inherited private model" to store the personalization experience.
CLAF: Contrastive Learning with Augmented Features for Imbalanced Semi-Supervised Learning
For each pseudo-labeled sample, we select positive and negative samples from labeled data instead of unlabeled data to compute contrastive loss.
MrTF: Model Refinery for Transductive Federated Learning
We propose a novel learning paradigm named transductive federated learning (TFL) to simultaneously consider the structural information of the to-be-inferred data.
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again
Complex teachers tend to be over-confident and traditional temperature scaling limits the efficacy of {\it class discriminability}, resulting in less discriminative wrong class probabilities.
Avoid Overfitting User Specific Information in Federated Keyword Spotting
Federated KWS (FedKWS) could serve as a solution without directly sharing users' data.
Federated Learning with Position-Aware Neurons
The permutation invariance property of neural networks and the non-i. i. d.
Preliminary Steps Towards Federated Sentiment Classification
Automatically mining sentiment tendency contained in natural language is a fundamental research to some artificial intelligent applications, where solutions alternate with challenges.
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes
We advocate the proposed methods could serve as a preliminary try to explore where to privatize for a novel non-iid scene.
