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Found 5 papers, 1 papers with code
Rethinking Query, Key, and Value Embedding in Vision Transformer under Tiny Model Constraints
The proposed method achieved $71. 4\%$ with a few parameters (of $3. 1M$) on the ImageNet-1k dataset compared to that required by the original transformer model of XCiT-N12 ($69. 9\%$).
Extending Contrastive Learning to Unsupervised Coreset Selection
Self-supervised contrastive learning offers a means of learning informative features from a pool of unlabeled data.
Less-forgetful Learning for Domain Expansion in Deep Neural Networks
Expanding the domain that deep neural network has already learned without accessing old domain data is a challenging task because deep neural networks forget previously learned information when learning new data from a new domain.
Less-forgetting Learning in Deep Neural Networks
Surprisingly, our method is very effective to forget less of the information in the source domain, and we show the effectiveness of our method using several experiments.
Rigid Motion Segmentation using Randomized Voting
For evaluation of our algorithm, Hopkins 155 dataset, which is a representative test set for rigid motion segmentation, is adopted; it consists of two and three rigid motions.
