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Found 11 papers, 4 papers with code
Enhanced Few-Shot Class-Incremental Learning via Ensemble Models
To tackle overfitting challenge, we design a new ensemble model framework cooperated with data augmentation to boost generalization.
Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs
In this paper, we propose GraphPrompt, a novel pre-training and prompting framework on graphs.
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework.
Maximize the Exploration of Congeneric Semantics for Weakly Supervised Semantic Segmentation
The graph network that is established with patches as the nodes can maximize the mutual learning of similar objects.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
Adversarial Learning with Cost-Sensitive Classes
This paper proposes a framework combining cost-sensitive classification and adversarial learning together to train a model that can distinguish between protected and unprotected classes, such that the protected classes are less vulnerable to adversarial examples.
Incorporating Hidden Layer representation into Adversarial Attacks and Defences
In this paper, we propose a defence strategy to improve adversarial robustness by incorporating hidden layer representation.
A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks
This paper points out a changing tendency of uncertainty in the convolutional layers of LeNet structure, and gives some insights to the interpretability of convolution.
Semi-supervised Breast Lesion Detection in Ultrasound Video Based on Temporal Coherence
Breast lesion detection in ultrasound video is critical for computer-aided diagnosis.
TAN: Temporal Affine Network for Real-Time Left Ventricle Anatomical Structure Analysis Based on 2D Ultrasound Videos
Instead of using three networks with one dedicating to each task, we use a multi-task network to perform three tasks simultaneously.
Med3D: Transfer Learning for 3D Medical Image Analysis
The performance on deep learning is significantly affected by volume of training data.
