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Found 10 papers, 4 papers with code
A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images
Nevertheless, the effectiveness of CL is highly dependent on the quality of the positive and negative sample pairs.
Self-supervised Domain Adaptation for Breaking the Limits of Low-quality Fundus Image Quality Enhancement
Most of the existing image enhancement methods mainly focus on improving the image quality by leveraging the guidance of high-quality images, which is difficult to be collected in medical applications.
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
Combining the strengths of UMIX with CMT, UCMT can retain model disagreement and enhance the quality of pseudo labels for the co-training segmentation.
UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer
Specifically, the CTrans module is an alternate of the U-Net skip connections, which consists of a sub-module to conduct the multi-scale Channel Cross fusion with Transformer (named CCT) and a sub-module Channel-wise Cross-Attention (named CCA) to guide the fused multi-scale channel-wise information to effectively connect to the decoder features for eliminating the ambiguity.
Ranked #2 on
Medical Image Segmentation
on GlaS
(IoU metric)
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD).
Ranked #1 on
Salient Object Detection
on SOD
Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning
Algorithmic trading, due to its inherent nature, is a difficult problem to tackle; there are too many variables involved in the real world which make it almost impossible to have reliable algorithms for automated stock trading.
Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems
Self-attentional models are a new paradigm for sequence modelling tasks which differ from common sequence modelling methods, such as recurrence-based and convolution-based sequence learning, in the way that their architecture is only based on the attention mechanism.
Contrastive Reasons Detection and Clustering from Online Polarized Debate
This work tackles the problem of unsupervised modeling and extraction of the main contrastive sentential reasons conveyed by divergent viewpoints on polarized issues.
Comparing Deep Reinforcement Learning and Evolutionary Methods in Continuous Control
Reinforcement Learning and the Evolutionary Strategy are two major approaches in addressing complicated control problems.
Complexity Analysis Approach for Prefabricated Construction Products Using Uncertain Data Clustering
This paper proposes an uncertain data clustering approach to quantitatively analyze the complexity of prefabricated construction components through the integration of quality performance-based measures with associated engineering design information.
