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Found 9 papers, 2 papers with code
Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data
Deep semi-supervised learning (SSL) has been shown very effectively.
Investigating the Limitation of CLIP Models: The Worst-Performing Categories
Contrastive Language-Image Pre-training (CLIP) provides a foundation model by integrating natural language into visual concepts, enabling zero-shot recognition on downstream tasks.
USB: A Unified Semi-supervised Learning Benchmark for Classification
We further provide the pre-trained versions of the state-of-the-art neural models for CV tasks to make the cost affordable for further tuning.
LAMDA-SSL: Semi-Supervised Learning in Python
The second part shows the usage of LAMDA-SSL by abundant examples in detail.
Transfer and Share: Semi-Supervised Learning from Long-Tailed Data
TRAS transforms the imbalanced pseudo-label distribution of a traditional SSL model via a delicate function to enhance the supervisory signals for minority classes.
Robust Deep Semi-Supervised Learning: A Brief Introduction
Semi-supervised learning (SSL) is the branch of machine learning that aims to improve learning performance by leveraging unlabeled data when labels are insufficient.
STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data
However, in many real-world applications, it is desirable to have SSL algorithms that not only classify the samples drawn from the same distribution of labeled data but also detect out-of-distribution (OOD) samples drawn from an unknown distribution.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Weakly Supervised Learning Meets Ride-Sharing User Experience Enhancement
For example, in user experience enhancement from Didi, one of the largest online ride-sharing platforms, the ride comment data contains severe label noise (due to the subjective factors of passengers) and severe label distribution bias (due to the sampling bias).
Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness
We guide the optimization of label quality through a small amount of validation data, and to ensure the safeness of performance while maximizing performance gain.
