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Low Rank JackUpdate on my joint work with @JulianTachella on "#Learning to Reconstruct Signals From Binary Measurements" on arXiv. #RandomProjection #onebit #SelfSupervisedLearning https://arxiv.org/abs/2303.08691
We brought several improvements on the proofs and the bounds allowing us to determine from how many binarized (random) projections, only, one can learn, up to a controlled identification error, a low-complexity space (with small box dimemsion). Moreover, a practical #selfsupervised scheme, SSBM, run over real datasets of images, enables to learn a reconstruction algorithm from those same binary observations (without access to the original images and on par with supervised alternatives), implicitly confirming the encoding of a good estimate of the image set.
Learning to Reconstruct Signals From Binary Measurements
Recent advances in unsupervised learning have highlighted the possibility of learning to reconstruct signals from noisy and incomplete linear measurements alone. These methods play a key role in medical and scientific imaging and sensing, where ground truth data is often scarce or difficult to obtain. However, in practice, measurements are not only noisy and incomplete but also quantized. Here we explore the extreme case of learning from binary observations and provide necessary and sufficient conditions on the number of measurements required for identifying a set of signals from incomplete binary data. Our results are complementary to existing bounds on signal recovery from binary measurements. Furthermore, we introduce a novel self-supervised learning approach, which we name SSBM, that only requires binary data for training. We demonstrate in a series of experiments with real datasets that SSBM performs on par with supervised learning and outperforms sparse reconstruction methods with a fixed wavelet basis by a large margin.
Clara Listensprechen
