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cs.LG2024
Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery
Anand Gopalakrishnan, Aleksandar Stanić, Jürgen Schmidhuber +1
Current state-of-the-art synchrony-based models encode object bindings with complex-valued activations and compute with real-valued weights in feedforward architectures. We argue f…
cs.LG2023★ 1 cited
Contrastive Training of Complex-Valued Autoencoders for Object Discovery
Aleksandar Stanić, Anand Gopalakrishnan, Kazuki Irie +1
Current state-of-the-art object-centric models use slots and attention-based routing for binding. However, this class of models has several conceptual limitations: the number of sl…