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Found 5 papers, 1 papers with code
Contrastive Continuity on Augmentation Stability Rehearsal for Continual Self-Supervised Learning
In order to address catastrophic forgetting without overfitting on the rehearsal samples, we propose Augmentation Stability Rehearsal (ASR) in this paper, which selects the most representative and discriminative samples by estimating the augmentation stability for rehearsal.
Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising
Experiment results demonstrate that the proposed method requires a reduced amount of training data to achieve high reconstruction quality among the state-of-art of MR reconstruction utilizing the Noise2Noise method.
Decoupled Self Attention for Accurate One Stage Object Detection
When DSA module and object confidence task are applied in RetinaNet together, the detection performances based on ResNet50 and ResNet101 can be increased by 1. 0% AP and 1. 4% AP respectively.
SuperDTI: Ultrafast diffusion tensor imaging and fiber tractography with deep learning
We also demonstrate that the trained neural network is robust to noise and motion in the testing data, and the network trained using healthy volunteer data can be directly applied to stroke patient data without compromising the lesion detectability.
A Random Sample Partition Data Model for Big Data Analysis
In this paper, we propose the random sample partition (RSP) data model to represent a big data set as a set of non-overlapping data subsets, called RSP data blocks, where each RSP data block has a probability distribution similar to the whole big data set.
