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On one hand, purely unsupervised approaches have proven successful on fully disentangled synthetic data, but fail to recover semantic factors from real data without strong inductive biases. On the other hand, supervised approaches are unstable and hard to scale to large attribute sets because they rely on adversarial objectives or auxiliary classifiers.\n  We introduce \\textsc{XFactors}, a weakly-supervised VAE framework that disentangles and provides explicit control over a chosen set of factors. Building on the Disentangled Information Bottleneck perspective, we decompose the representation into a residual subspace $\\mathcal{S}$ and factor-specific subspaces $\\mathcal{T}_1,\\ldots,\\mathcal{T}_K$ and a residual subspace $\\mathcal{S}$. Each target factor is encoded in its assigned $\\mathcal{T}_i$ through con","title":"XFACTORS: Disentangled Information Bottleneck via Contrastive Supervision","url":"https://arxiv.org/abs/2601.21688","vendor":"arxiv_cs_ai"},"summary":"arXiv:2601.21688v2 Announce Type: replace-cross \nAbstract: Disentangled representation learning aims to map independent factors of variation to independent representation components. On one hand, purely unsupervised approaches have proven successful on fully disentangled synthetic data, but fail to recover semantic factors from real data without strong inductive biases. On the other hand, supervised approaches are unstable and hard to scale to large attribute sets because they rely on adversarial objectives or auxiliary classifiers.\n  We introduce \\textsc{XFactors}, a weakly-supervised VAE framework that disentangles and provides explicit control over a chosen set of factors. Building on the Disentangled Information Bottleneck perspective, we decompose the representation into a residual subspace $\\mathcal{S}$ and factor-specific subspaces $\\mathcal{T}_1,\\ldots,\\mathcal{T}_K$ and a residual subspace $\\mathcal{S}$. 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