CliqueCNN: Deep Unsupervised Exemplar Learning

TL;DR

CliqueCNN introduces an unsupervised deep learning method that groups samples into cliques to learn visual similarities without labels, effectively handling the imbalance and unreliable relations in exemplar learning.

Contribution

It proposes a novel clique-based optimization framework for unsupervised exemplar similarity learning using deep CNNs, eliminating the need for labeled data.

Findings

Achieved competitive results in posture analysis.

Performed well in object classification tasks.

Effectively grouped samples into meaningful cliques.

Abstract

Exemplar learning is a powerful paradigm for discovering visual similarities in an unsupervised manner. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single positive sample, a great imbalance between one positive and many negatives, and unreliable relationships between most samples, training of Convolutional Neural networks is impaired. Given weak estimates of local distance we propose a single optimization problem to extract batches of samples with mutually consistent relations. Conflicting relations are distributed over different batches and similar samples are grouped into compact cliques. Learning exemplar similarities is framed as a sequence of clique categorization tasks. The CNN then consolidates transitivity relations within and between cliques and learns a single representation for all samples without the need for labels. The proposed unsupervised approach has shown competitive performance on detailed posture analysis and object classification.