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Found 12 papers, 3 papers with code
FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning
Intelligent equipment fault diagnosis based on Federated Transfer Learning (FTL) attracts considerable attention from both academia and industry.
Generative Model-based Feature Knowledge Distillation for Action Recognition
Addressing this gap, our paper introduces an innovative knowledge distillation framework, with the generative model for training a lightweight student model.
Controlled Randomness Improves the Performance of Transformer Models
During the pre-training step of natural language models, the main objective is to learn a general representation of the pre-training dataset, usually requiring large amounts of textual data to capture the complexity and diversity of natural language.
Weakly-Supervised Action Localization by Hierarchically-structured Latent Attention Modeling
To address this problem, we propose a novel attention-based hierarchically-structured latent model to learn the temporal variations of feature semantics.
ACE: Towards Application-Centric Edge-Cloud Collaborative Intelligence
Intelligent applications based on machine learning are impacting many parts of our lives.
HEU Emotion: A Large-scale Database for Multi-modal Emotion Recognition in the Wild
Existing multimodal emotion databases in the real-world conditions are few and small, with a limited number of subjects and expressed in a single language.
CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning
The emerging edge-cloud collaborative Deep Learning (DL) paradigm aims at improving the performance of practical DL implementations in terms of cloud bandwidth consumption, response latency, and data privacy preservation.
OL4EL: Online Learning for Edge-cloud Collaborative Learning on Heterogeneous Edges with Resource Constraints
However, heterogeneous and limited computation and communication resources on edge servers (or edges) pose great challenges on distributed ML and formulate a new paradigm of Edge Learning (i. e. edge-cloud collaborative machine learning).
Asynchronous Federated Learning with Differential Privacy for Edge Intelligence
Formally, we give the first analysis on the model convergence of AFL under DP and propose a multi-stage adjustable private algorithm (MAPA) to improve the trade-off between model utility and privacy by dynamically adjusting both the noise scale and the learning rate.
Neural Architecture Refinement: A Practical Way for Avoiding Overfitting in NAS
Based on this correlation, we further demonstrate that, though the reward of NAS is sparse, the policy gradient method implicitly assign the reward to all operations and skip connections based on the sampling frequency.
Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning
Rich high-quality annotated data is critical for semantic segmentation learning, yet acquiring dense and pixel-wise ground-truth is both labor- and time-consuming.
Towards Accurate Binary Convolutional Neural Network
We introduce a novel scheme to train binary convolutional neural networks (CNNs) -- CNNs with weights and activations constrained to {-1,+1} at run-time.
