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08/29/2023
Cross-Modal Retrieval Meets Inference:Improving Zero-Shot Classification with Cross-Modal Retrieval
Contrastive language-image pre-training (CLIP) has demonstrated remarkab...
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08/24/2023
FedSoL: Bridging Global Alignment and Local Generality in Federated Learning
Federated Learning (FL) aggregates locally trained models from individua...
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10/18/2022
Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks
Translation has played a crucial role in improving the performance on mu...
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06/18/2022
Demystifying the Base and Novel Performances for Few-shot Class-incremental Learning
Few-shot class-incremental learning (FSCIL) has addressed challenging re...
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05/13/2022
Revisiting the Updates of a Pre-trained Model for Few-shot Learning
Most of the recent few-shot learning algorithms are based on transfer le...
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05/11/2022
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning
Cross-domain few-shot learning (CD-FSL), where there are few target samp...
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02/01/2022
Understanding Cross-Domain Few-Shot Learning: An Experimental Study
Cross-domain few-shot learning has drawn increasing attention for handli...
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05/19/2021
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation
Knowledge distillation (KD), transferring knowledge from a cumbersome te...
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12/09/2020
Accurate and Fast Federated Learning via IID and Communication-Aware Grouping
Federated learning has emerged as a new paradigm of collaborative machin...
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12/06/2020
TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture
Federated learning has emerged as a new paradigm of collaborative machin...
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08/20/2020
Does MAML really want feature reuse only?
Meta-learning, the effort to solve new tasks with only a few samples, ha...
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10/26/2018
