2,440 research outputs found
Semi-Supervised Learning with Scarce Annotations
While semi-supervised learning (SSL) algorithms provide an efficient way to
make use of both labelled and unlabelled data, they generally struggle when the
number of annotated samples is very small. In this work, we consider the
problem of SSL multi-class classification with very few labelled instances. We
introduce two key ideas. The first is a simple but effective one: we leverage
the power of transfer learning among different tasks and self-supervision to
initialize a good representation of the data without making use of any label.
The second idea is a new algorithm for SSL that can exploit well such a
pre-trained representation.
The algorithm works by alternating two phases, one fitting the labelled
points and one fitting the unlabelled ones, with carefully-controlled
information flow between them. The benefits are greatly reducing overfitting of
the labelled data and avoiding issue with balancing labelled and unlabelled
losses during training. We show empirically that this method can successfully
train competitive models with as few as 10 labelled data points per class. More
in general, we show that the idea of bootstrapping features using
self-supervised learning always improves SSL on standard benchmarks. We show
that our algorithm works increasingly well compared to other methods when
refining from other tasks or datasets.Comment: Workshop on Deep Vision, CVPR 202
LaplaceNet: A Hybrid Energy-Neural Model for Deep Semi-Supervised Classification
Semi-supervised learning has received a lot of recent attention as it
alleviates the need for large amounts of labelled data which can often be
expensive, requires expert knowledge and be time consuming to collect. Recent
developments in deep semi-supervised classification have reached unprecedented
performance and the gap between supervised and semi-supervised learning is
ever-decreasing. This improvement in performance has been based on the
inclusion of numerous technical tricks, strong augmentation techniques and
costly optimisation schemes with multi-term loss functions. We propose a new
framework, LaplaceNet, for deep semi-supervised classification that has a
greatly reduced model complexity. We utilise a hybrid energy-neural network
where graph based pseudo-labels, generated by minimising the graphical
Laplacian, are used to iteratively improve a neural-network backbone. Our model
outperforms state-of-the-art methods for deep semi-supervised classification,
over several benchmark datasets. Furthermore, we consider the application of
strong-augmentations to neural networks theoretically and justify the use of a
multi-sampling approach for semi-supervised learning. We demonstrate, through
rigorous experimentation, that a multi-sampling augmentation approach improves
generalisation and reduces the sensitivity of the network to augmentation
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy
Regularization plays a crucial role in machine learning models, especially
for deep neural networks. The existing regularization techniques mainly reply
on the i.i.d. assumption and only employ the information of the current sample,
without the leverage of neighboring information between samples. In this work,
we propose a general regularizer called Patch-level Neighborhood
Interpolation~(\textbf{Pani}) that fully exploits the relationship between
samples. Furthermore, by explicitly constructing a patch-level graph in the
different network layers and interpolating the neighborhood features to refine
the representation of the current sample, our Patch-level Neighborhood
Interpolation can then be applied to enhance two popular regularization
strategies, namely Virtual Adversarial Training (VAT) and MixUp, yielding their
neighborhood versions. The first derived \textbf{Pani VAT} presents a novel way
to construct non-local adversarial smoothness by incorporating patch-level
interpolated perturbations. In addition, the \textbf{Pani MixUp} method extends
the original MixUp regularization to the patch level and then can be developed
to MixMatch, achieving the state-of-the-art performance. Finally, extensive
experiments are conducted to verify the effectiveness of the Patch-level
Neighborhood Interpolation in both supervised and semi-supervised settings
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