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Found 5 papers, 0 papers with code
Jaeger: A Concatenation-Based Multi-Transformer VQA Model
By leveraging pre-trained models for feature extraction, our approach has the potential to amplify the performance of these models through concatenation.
Device Tuning for Multi-Task Large Model
Unsupervised pre-training approaches have achieved great success in many fields such as Computer Vision (CV), Natural Language Processing (NLP) and so on.
Deep Transfer Tensor Factorization for Multi-View Learning
To solve data sparsity problem in multiview ratings, we propose a generic architecture of deep transfer tensor factorization (DTTF) by integrating deep learning and cross-domain tensor factorization, where the side information is embedded to provide effective compensation for the tensor sparsity.
Robust Meta Learning for Image based tasks
However, learning such a model is not possible in standard machine learning frameworks as the distribution of the test data is unknown.
Invariant Meta Learning for Out-of-Distribution Generalization
Modern deep learning techniques have illustrated their excellent capabilities in many areas, but relies on large training data.
