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Found 5 papers, 2 papers with code
Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs
Considering the NIE problem, LICAP adopts a novel sampling strategy called top nodes preferred hierarchical sampling to first group all interested nodes into a top bin and a non-top bin based on node importance scores, and then divide the nodes within top bin into several finer bins also based on the scores.
Federated Learning with Classifier Shift for Class Imbalance
Federated learning aims to learn a global model collaboratively while the training data belongs to different clients and is not allowed to be exchanged.
Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews
This framework is designed for general fossil identification and it is expected to see applications to other fossil datasets in future work.
Learning to Decompose Visual Features with Latent Textual Prompts
Recent advances in pre-training vision-language models like CLIP have shown great potential in learning transferable visual representations.
Boost Neural Networks by Checkpoints
In the experiments, it yields up to 5. 02% higher accuracy over single EfficientNet-B0 on the imbalanced datasets.
