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Found 4 papers, 0 papers with code
Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers
Through extensive experiments on various language models and complexity analysis, we demonstrate that aespa is accurate and efficient in quantizing Transformer models.
Vision Transformer-based Feature Extraction for Generalized Zero-Shot Learning
In AAM, the correlation between each patch feature and the synthetic image attribute is used as the importance weight for each patch.
Semantic Feature Extraction for Generalized Zero-shot Learning
Key idea of the proposed approach, henceforth referred to as semantic feature extraction-based GZSL (SE-GZSL), is to use the semantic feature containing only attribute-related information in learning the relationship between the image and the attribute.
Gradual Federated Learning with Simulated Annealing
Federated averaging (FedAvg) is a popular federated learning (FL) technique that updates the global model by averaging local models and then transmits the updated global model to devices for their local model update.
