LLID21
* *Learning From Limited or Imperfect Data
* BalaGAN: Cross-Modal Image Translation Between Imbalanced Domains
* Boosting Co-teaching with Compression Regularization for Label Noise
* Boosting Unconstrained Face Recognition with Auxiliary Unlabeled Data
* Closer Look at Self-training for Zero-Label Semantic Segmentation, A
* Cluster-driven Graph Federated Learning over Multiple Domains
* Contrastive Learning Improves Model Robustness Under Label Noise
* DAMSL: Domain Agnostic Meta Score-based Learning
* Distill on the Go: Online knowledge distillation in self-supervised learning
* Efficacy of Bayesian Neural Networks in Active Learning
* Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation
* Improving Semi-Supervised Domain Adaptation Using Effective Target Selection and Semantics
* Learning from Incomplete Features by Simultaneous Training of Neural Networks and Sparse Coding
* Learning Unbiased Representations via Mutual Information Backpropagation
* One-shot action recognition in challenging therapy scenarios
* One-Shot GAN: Learning to Generate Samples from Single Images and Videos
* PLM: Partial Label Masking for Imbalanced Multi-label Classification
* ReMP: Rectified Metric Propagation for Few-Shot Learning
* Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaption
* Shot in the Dark: Few-Shot Learning with No Base-Class Labels
* TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition
* Training Deep Generative Models in Highly Incomplete Data Scenarios with Prior Regularization
* Training Rare Object Detection in Satellite Imagery with Synthetic GAN Images
* Unlocking the Full Potential of Small Data with Diverse Supervision
* Weak Multi-View Supervision for Surface Mapping Estimation
25 for LLID21
LLID22
* *Learning From Limited or Imperfect Data
* Learning from Noisy Labels with Coarse-to-fine Sample Credibility Modeling
* Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super Resolution
* Open-vocabulary Semantic Segmentation Using Test-time Distillation
* Opencos: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Data
* Out-of-distribution Detection Without Class Labels
* Plmcl: Partial-label Momentum Curriculum Learning for Multi-label Image Classification
* Semi-supervised Domain Adaptation by Similarity Based Pseudo-label Injection
* Sitta: Single Image Texture Translation for Data Augmentation
* SW-VAE: Weakly Supervised Learn Disentangled Representation via Latent Factor Swapping
* Unsupervised Domain Adaptive Object Detection with Class Label Shift Weighted Local Features
11 for LLID22