SSNG replaces sampling-based updates in MHNG with symmetric self-supervised representation alignment using Gumbel-Softmax for discrete messages, yielding higher linear-probe classification accuracy on CIFAR-10 and ImageNet-100 than referential, reconstruction, or MHNG baselines.
A survey on self-supervised representation learning
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Combining diverse feature and graph representations from multiple extractors with GNNs and rank aggregation improves semi-supervised image classification accuracy.
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Graph Neural Networks for Semi-Supervised Image Classification with Multi-Feature Aggregation
Combining diverse feature and graph representations from multiple extractors with GNNs and rank aggregation improves semi-supervised image classification accuracy.