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Found 7 papers, 5 papers with code
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one.
ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring
We improve the recently-proposed ``MixMatch semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring.
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance.
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
Distribution alignment encourages the marginal distribution of predictions on unlabeled data to be close to the marginal distribution of ground-truth labels.
High Accuracy and High Fidelity Extraction of Neural Networks
In a model extraction attack, an adversary steals a copy of a remotely deployed machine learning model, given oracle prediction access.
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich.
Large-Scale Evolution of Image Classifiers
Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone.
Ranked #100 on
Image Classification
on CIFAR-10
