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09/19/2023
Decoupled Training: Return of Frustratingly Easy Multi-Domain Learning
Multi-domain learning (MDL) aims to train a model with minimal average r...
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08/16/2023
STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
Multi-task learning (MTL) has gained significant popularity in recommend...
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02/02/2023
CLIPood: Generalizing CLIP to Out-of-Distributions
Out-of-distribution (OOD) generalization, where the model needs to handl...
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01/30/2023
ForkMerge: Overcoming Negative Transfer in Multi-Task Learning
The goal of multi-task learning is to utilize useful knowledge from mult...
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11/28/2022
AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning
Multi-task learning (MTL) models have demonstrated impressive results in...
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02/15/2022
Debiased Pseudo Labeling in Self-Training
Deep neural networks achieve remarkable performances on a wide range of ...
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10/09/2021
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model
To mitigate the burden of data labeling, we aim at improving data effici...
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03/10/2021
Regressive Domain Adaptation for Unsupervised Keypoint Detection
Domain adaptation (DA) aims at transferring knowledge from a labeled sou...
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02/25/2021
Self-Tuning for Data-Efficient Deep Learning
Deep learning has made revolutionary advances to diverse applications in...
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11/12/2020
Bi-tuning of Pre-trained Representations
It is common within the deep learning community to first pre-train a dee...
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07/16/2020
Transferable Calibration with Lower Bias and Variance in Domain Adaptation
Domain Adaptation (DA) enables transferring a learning machine from a la...
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12/08/2019
