Search Results for author:
Found 7 papers, 4 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.
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.
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Then we present a novel scheme for liver lesion classification using CNN.
Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN
Automatic detection of liver lesions in CT images poses a great challenge for researchers.
