13,409 research outputs found
Semi-Supervised Learning by Augmented Distribution Alignment
In this work, we propose a simple yet effective semi-supervised learning
approach called Augmented Distribution Alignment. We reveal that an essential
sampling bias exists in semi-supervised learning due to the limited number of
labeled samples, which often leads to a considerable empirical distribution
mismatch between labeled data and unlabeled data. To this end, we propose to
align the empirical distributions of labeled and unlabeled data to alleviate
the bias. On one hand, we adopt an adversarial training strategy to minimize
the distribution distance between labeled and unlabeled data as inspired by
domain adaptation works. On the other hand, to deal with the small sample size
issue of labeled data, we also propose a simple interpolation strategy to
generate pseudo training samples. Those two strategies can be easily
implemented into existing deep neural networks. We demonstrate the
effectiveness of our proposed approach on the benchmark SVHN and CIFAR10
datasets. Our code is available at \url{https://github.com/qinenergy/adanet}.Comment: To appear in ICCV 201
Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images
We propose a framework for the automatic one-shot segmentation of synthetic
images generated by a StyleGAN. Our framework is based on the observation that
the multi-scale hidden features in the GAN generator hold useful semantic
information that can be utilized for automatic on-the-fly segmentation of the
generated images. Using these features, our framework learns to segment
synthetic images using a self-supervised contrastive clustering algorithm that
projects the hidden features into a compact space for per-pixel classification.
This contrastive learner is based on using a novel data augmentation strategy
and a pixel-wise swapped prediction loss that leads to faster learning of the
feature vectors for one-shot segmentation. We have tested our implementation on
five standard benchmarks to yield a segmentation performance that not only
outperforms the semi-supervised baselines by an average wIoU margin of 1.02 %
but also improves the inference speeds by a factor of 4.5. Finally, we also
show the results of using the proposed one-shot learner in implementing BagGAN,
a framework for producing annotated synthetic baggage X-ray scans for threat
detection. This framework was trained and tested on the PIDRay baggage
benchmark to yield a performance comparable to its baseline segmenter based on
manual annotations
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