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Found 8 papers, 6 papers with code
Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning
Running a dedicated model for each task is computationally expensive and therefore there is a great interest in multi-task learning (MTL).
De-Confusing Pseudo-Labels in Source-Free Domain Adaptation
In this paper, we introduce a novel noise-learning approach tailored to address noise distribution in domain adaptation settings and learn to de-confuse the pseudo-labels.
EPTQ: Enhanced Post-Training Quantization via Label-Free Hessian
Quantization of deep neural networks (DNN) has become a key element in the efforts of embedding such networks on end-user devices.
Reconciling a Centroid-Hypothesis Conflict in Source-Free Domain Adaptation
We propose to reconcile this conflict by aligning the entropy minimization objective with that of the pseudo labels' cross entropy.
HPTQ: Hardware-Friendly Post Training Quantization
Neural network quantization enables the deployment of models on edge devices.
Ranked #1 on
Quantization
on MS COCO
Multi-View Image-to-Image Translation Supervised by 3D Pose
Experimental results on the CMU-Panoptic dataset demonstrate the effectiveness of the suggested framework in generating photo-realistic images of persons with new poses that are more consistent across all views in comparison to a standard Image-to-Image baseline.
HMQ: Hardware Friendly Mixed Precision Quantization Block for CNNs
In this work, we introduce the Hardware Friendly Mixed Precision Quantization Block (HMQ) in order to meet this requirement.
Ranked #13 on
Quantization
on ImageNet
Asymmetric Distributed Constraint Optimization Problems
Innovative algorithms that apply to the special properties of the proposed ADCOP model are presented in detail.
