Deep learning has demonstrated abilities to learn complex structures, but they can be restricted by available data. Recently, Consensus Networks (CNs) were proposed to alleviate data sparsity by utilizing features from multiple modalities, but they too have been limited by the size of labeled data. In this paper, we extend CN to Transductive Consensus Networks (TCNs), suitable for semi-supervised learning. In TCNs, different modalities of input are compressed into latent representations, which we encourage to become indistinguishable during iterative adversarial training. To understand TCNs two mechanisms, consensus and classification, we put forward its three variants in ablation studies on these mechanisms. To further investigate TCN models, we treat the latent representations as probability distributions and measure their similarities as the negative relative Jensen-Shannon divergences. We show that a consensus state beneficial for classification desires a stable but imperfect similarity between the representations. Overall, TCNs outperform or align with the best benchmark algorithms given 20 to 200 labeled samples on the Bank Marketing and the DementiaBank datasets.
@cite_10: Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization. It also acts as a regularizer, in some cases eliminating the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.9 top-5 validation error (and 4.8 test error), exceeding the accuracy of human raters.
@cite_8: Objective: We describe the sampling, initial evaluation, and final diagnostic classification of subjects enrolled in a natural history study of Alzheimer's disease (AD). Design: Volunteer cohort study. Setting: Multidisciplinary behavioral neurology research clinic. Patients or Other Participants: Three-hundred nineteen individuals were enrolled in the Alzheimer Research Program between March 1983 and March 1988. Of these, 204 were originally classified with AD, 102 were normal elderly control subjects, and 13 were considered special cases. Main Outcome Measures: Final consensus clinical diagnosis, final neuropathologic diagnosis, and death. Results: Of the 204 patients enrolled in the study, re-review after as many as 5 years of follow-up resulted in a final clinical classification of 188 with probable AD. Seven patients were believed to have a significant vascular component to the dementia, three were found to have developed depression, and six were excluded on other clinical grounds. Neuropathologic examination of 50 brains indicated definite AD in 43. After removing these seven misdiagnosed patients, the final group of probable definite AD totaled 181 individuals. Accuracy of the baseline clinical diagnosis relative to neuropathology was 86 , and when follow-up clinical data were considered, 91.4 . Detailed neuropsychological testingyielded high sensitivity (0.988) and specificity (0.983) to dementia. Analyses of survival time from study entry until death revealed that older patients were significantly more likely to die during follow-up, but neither sex, years of education, nor pattern of cognitive impairment were related to survival. Conclusions: These data provide the descriptive basis for future studies of this cohort. They indicate that longitudinal follow-up of demented cases increases accuracy of diagnosis, and that detailed cognitive testing aids in early classification.
In domain adaptation, some work has been applied to find a unified representation between domains, for example, by applying domain invariant training @cite_8 and semantic similarity loss @cite_10 . However, our approach does not involve multiple domains -- we only handle data from one domain. Here, the term domain' refers to how the data are naturally generated, whereas the term modality' refers to how different aspects of data are observed.