Public articles linked to the same research event.
arXiv The work proposes a fusion framework combining representations from three self-supervised objectives, MAE, JEPA, and SimCLR, and a multi-branch architecture with a shared trunk and objective-specific branches; after pretraining on roughly 25,000 spectrogram and CSI samples, at equal output embedding dimension fusion outperforms the evaluated single-objective encoders on all six downstream tasks spanning communication, sensing, and positioning while using about one-third of their parameters, and the multi-branch architecture retains much of the fusion benefit within a single-encoder parameter budget, with representation analyses indicating complementary contributions across objectives and much of the added benefit retained in components orthogonal to MAE's representation subspace.
The work proposes a fusion framework combining representations from three self-supervised objectives, MAE, JEPA, and SimCLR, and a multi-branch architecture with a shared trunk and objective-specific branches; after pretraining on roughly 25,000 spectrogram and CSI samples, at equal output embedding dimension fusion outperforms the evaluated single-objective encoders on all six downstream tasks spanning communication, sensing, and positioning while using about one-third of their parameters, and the multi-branch architecture retains much of the fusion benefit within a single-encoder parameter budget, with representation analyses indicating complementary contributions across objectives and much of the added benefit retained in components orthogonal to MAE's representation subspace.
The work proposes a fusion framework combining representations from three self-supervised objectives, MAE, JEPA, and SimCLR, and a multi-branch architecture with a shared trunk and objective-specific branches; after pretraining on roughly 25,000 spectrogram and CSI samples, at equal output embedding dimension fusion outperforms the evaluated single-objective encoders on all six downstream tasks spanning communication, sensing, and positioning while using about one-third of their parameters, and the multi-branch architecture retains much of the fusion benefit within a single-encoder parameter budget, with representation analyses indicating complementary contributions across objectives and much of the added benefit retained in components orthogonal to MAE's representation subspace.
The work proposes a fusion framework combining representations from three self-supervised objectives, MAE, JEPA, and SimCLR, and a multi-branch architecture with a shared trunk and objective-specific branches; after pretraining on roughly 25,000 spectrogram and CSI samples, at equal output embedding dimension fusion outperforms the evaluated single-objective encoders on all six downstream tasks spanning communication, sensing, and positioning while using about one-third of their parameters, and the multi-branch architecture retains much of the fusion benefit within a single-encoder parameter budget, with representation analyses indicating complementary contributions across objectives and much of the added benefit retained in components orthogonal to MAE's representation subspace.