Search Results for author:
Found 17 papers, 4 papers with code
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.
Parameter-Efficient Long-Tailed Recognition
In this paper, we propose PEL, a fine-tuning method that can effectively adapt pre-trained models to long-tailed recognition tasks in fewer than 20 epochs without the need for extra data.
Ranked #1 on
Long-tail Learning
on CIFAR-100-LT (ρ=10)
(using extra training data)
Fine-Grained Image Classification
Long-tail learning with class descriptors
A Survey on Extreme Multi-label Learning
Multi-label learning has attracted significant attention from both academic and industry field in recent decades.
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.
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
Prototypical Classifier for Robust Class-Imbalanced Learning
Deep neural networks have been shown to be very powerful methods for many supervised learning tasks.
Dash: Semi-Supervised Learning with Dynamic Thresholding
In this work we develop a simple yet powerful framework, whose key idea is to select a subset of training examples from the unlabeled data when performing existing SSL methods so that only the unlabeled examples with pseudo labels related to the labeled data will be used to train models.
Robust Long-Tailed Learning under Label Noise
To overcome this limitation, we establish a new prototypical noise detection method by designing a distance-based metric that is resistant to label noise.
Ranked #25 on
Image Classification
on mini WebVision 1.0
NGC: A Unified Framework for Learning with Open-World Noisy Data
The existence of noisy data is prevalent in both the training and testing phases of machine learning systems, which inevitably leads to the degradation of model performance.
Ranked #18 on
Image Classification
on mini WebVision 1.0
Improving Tail Label Prediction for Extreme Multi-label Learning
Extreme multi-label learning (XML) works to annotate objects with relevant labels from an extremely large label set.
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.
Deep learning based automatic segmentation of lumbosacral nerves on non-contrast CT for radiographic evaluation: a pilot study
Pixel accuracy, IoU, and Dice score are used to assess the segmentation performance of lumbosacral structures.
Convex and Scalable Weakly Labeled SVMs
In this paper, we study the problem of learning from weakly labeled data, where labels of the training examples are incomplete.
Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison
Both random Fourier features and the Nyström method have been successfully applied to efficient kernel learning.
