传统的attention和cross attention具有较高的计算复杂性,而cross attention是一种基于MHSA的方法,通常需要大量的数据进行训练。表9显示了传统的attention、cross attention和我们提出的特征融合方法在参数数量方面的比较。从表9中可以看出,与其他方法相比,我们在MAMIL中提出的轻量级特征融合方法显著减少了参数数量。 图7 图8...
Deep learningSimilarity-based lossAttention-based poolingInterpretabilityAttention-based deep multi-instance learning (MIL) is an effective and interpretable model. Its interpretability is attributed to the learnability of its inner attention-based MIL pooling. Its main problem is to learn a unique ...
Rich Caruana给出的MTL定义:“MTL is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared low dimensional representation; ...
2) an attention multi-instance learning (MIL) pooling operation for balancing the relative contribution of each patch and yield a global different weighted representation for the whole brain structure, and 3) an attention-aware global classifier for further learning the integral features and making th...
Multi-view learning has received much attention in machine learning, especially for multi-label and image classification [3], [4], [5]. Learning classification models from the fusion of multiple views increases the strength of the classification predictions as compared to independent views [6], [...
Recently, the attention mechanism allows the network to learn a dynamic and adaptive aggregation of the neighborhood. We propose a new GCN model on the graphs where edges are characterized in multiple views or precisely in terms of multiple relationships. For instance, in chemical graph theory, ...
Attention focusing 注意 Eavesdropping 窃听 Representation bias 表示偏向 Regularization 正则化 非神经网络模型中的MTL,主要有两种: Block-sparse regularization:enforcing sparsity across tasks through norm regularization Learning task relationships:modelling the relationships...
Attention-based deep multiple instance learning. In Proceedings of the 35th International Conference on Machine Learning, 2127–2136 (2018). Zaheer, M. et al. Deep sets. In Advances in Neural Information Processing Systems 30, 3391–3401 (NIPS, 2017). Bahdanau, D., Cho, K. & Bengio, Y....
Qian X, Yang G, Li F, Zhang X, Zhu X, Lai X, Xiao X, Wang T and Wang J (2024) DeepLION2: deep multi-instance contrastive learning framework enhancing the prediction of cancer-associated T cell receptors by attention strategy on motifs.Front. Immunol.15:1345586. doi: 10.3389/fimmu.202...
A Novel Automatic Image Annotation Method Based on Multi-instance Learning提出了一种基于多实例学习的图像自动标注方法外文翻译.doc,A Novel Automatic Image Annotation Method Based on Multi-instance Learning Abstract Automatic image annotation (AIA) is the