  Certain datasets contain a limited number of samples with highly various
styles and complex structures. This study presents a novel adversarial
Lagrangian integrated contrastive embedding (ALICE) method for small-sized
datasets. First, the accuracy improvement and training convergence of the
proposed pre-trained adversarial transfer are shown on various subsets of
datasets with few samples. Second, a novel adversarial integrated contrastive
model using various augmentation techniques is investigated. The proposed
structure considers the input samples with different appearances and generates
a superior representation with adversarial transfer contrastive training.
Finally, multi-objective augmented Lagrangian multipliers encourage the
low-rank and sparsity of the presented adversarial contrastive embedding to
adaptively estimate the coefficients of the regularizers automatically to the
optimum weights. The sparsity constraint suppresses less representative
elements in the feature space. The low-rank constraint eliminates trivial and
redundant components and enables superior generalization. The performance of
the proposed model is verified by conducting ablation studies by using
benchmark datasets for scenarios with small data samples.
