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Provable Compositional Generalization for Object-Centric Learning
Learning representations that generalize to novel compositions of known concepts is crucial for bridging the gap between human and machine perception.
Multi-step domain adaptation by adversarial attack to $\mathcal{H} Δ\mathcal{H}$-divergence
Adversarial examples are transferable between different models.
Connecting adversarial attacks and optimal transport for domain adaptation
In domain adaptation, the goal is to adapt a classifier trained on the source domain samples to the target domain.
Cycle monotonicity of adversarial attacks for optimal domain adaptation
In our algorithm, instead of mapping from target to the source domain, optimal transport maps target samples to the set of adversarial examples.
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