HybridNet: Classification and Reconstruction Cooperation for Semi-Supervised Learning
In this paper, we introduce a new model for leveraging unlabeled data to improve generalization performances of image classifiers: a two-branch encoder-decoder architecture called HybridNet. The first branch receives supervision signal and is dedicated to the extraction of invariant class-related representations. The second branch is fully unsupervised and dedicated to model information discarded by the first branch to reconstruct input data. To further support the expected behavior of our model, we propose an original training objective. It favors stability in the discriminative branch and complementarity between the learned representations in the two branches. HybridNet is able to outperform state-of-the-art results on CIFAR-10, SVHN and STL-10 in various semi-supervised settings. In addition, visualizations and ablation studies validate our contributions and the behavior of the model on both CIFAR-10 and STL-10 datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationDecoderGeneral ClassificationImage ClassificationSimilar Papers 제목 키워드 기반
Particle Competition and Cooperation for Semi-Supervised Learning with Label Noise
Semi-supervised learning methods are usually employed in the classification of data sets where only a small subset of the data items is labeled. In these scenarios, label noise is a crucial issue, since the noise may eas…
ClassificationGeneral ClassificationAttn-HybridNet: Improving Discriminability of Hybrid Features with Attention Fusion
The principal component analysis network (PCANet) is an unsupervised parsimonious deep network, utilizing principal components as filters in its convolution layers. Albeit powerful, the PCANet consists of basic operation…
HybridNet: A Hybrid Neural Architecture to Speed-up Autoregressive Models
This paper introduces HybridNet, a hybrid neural network to speed-up autoregressive models for raw audio waveform generation. As an example, we propose a hybrid model that combines an autoregressive network named WaveNet…
Speech Synthesistext-to-speechText to SpeechSemi-supervised time series classification method for quantum computing
In this paper we develop methods to solve two problems related to time series (TS) analysis using quantum computing: reconstruction and classification. We formulate the task of reconstructing a given TS from a training s…
ClassificationGeneral ClassificationSemi-supervised time series classificationTime Series+2Hybridnet for depth estimation and semantic segmentation
Semantic segmentation and depth estimation are two important tasks in the area of image processing. Traditionally, these two tasks are addressed in an independent manner. However, for those applications where geometric a…
Autonomous NavigationDepth EstimationSegmentationSemantic Segmentation