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Found 10 papers, 6 papers with code
Integration of Self-Supervised BYOL in Semi-Supervised Medical Image Recognition
Image recognition techniques heavily rely on abundant labeled data, particularly in medical contexts.
Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models Memories
Pre-trained language models (PLMs) demonstrate excellent abilities to understand texts in the generic domain while struggling in a specific domain.
Black-box Prompt Learning for Pre-trained Language Models
Particularly, instead of fine-tuning the model in the cloud, we adapt PLMs by prompt learning, which efficiently optimizes only a few parameters of the discrete prompts.
Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation
In this paper, we aim to adapt a generic pretrained model with a relatively small amount of domain-specific data.
Ranked #42 on
Time Series Forecasting
on ETTh1 (336) Multivariate
Exploring Geometric Consistency for Monocular 3D Object Detection
In addition, we demonstrate that the augmentation methods are well suited for semi-supervised training and cross-dataset generalization.
Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection
Domain adaptation for object detection tries to adapt the detector from labeled datasets to unlabeled ones for better performance.
An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation
In contrast to adversarial alignment, we propose to explicitly train a domain-invariant classifier by generating and defensing against pointwise feature space adversarial perturbations.
Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation
Domain adaptation enables the learner to safely generalize into novel environments by mitigating domain shifts across distributions.
Ranked #8 on
Domain Adaptation
on ImageCLEF-DA
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
Motivated by the theoretical results in \cite{mansour2009domain}, the target distribution can be represented as the weighted combination of source distributions, and, the multi-source unsupervised domain adaptation via DCTN is then performed as two alternating steps: i) It deploys multi-way adversarial learning to minimize the discrepancy between the target and each of the multiple source domains, which also obtains the source-specific perplexity scores to denote the possibilities that a target sample belongs to different source domains.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
Multi-label Image Recognition by Recurrently Discovering Attentional Regions
This paper proposes a novel deep architecture to address multi-label image recognition, a fundamental and practical task towards general visual understanding.
