Learning Canonical Transformations
Humans understand a set of canonical geometric transformations (such as translation and rotation) that support generalization by being untethered to any specific object. We explore inductive biases that help a neural network model learn these transformations in pixel space in a way that can generalize out-of-domain. Specifically, we find that high training set diversity is sufficient for the extrapolation of translation to unseen shapes and scales, and that an iterative training scheme achieves significant extrapolation of rotation in time.
Code (0)
등록된 구현이 없습니다.
Tasks
DiversityTranslationSimilar Papers 제목 키워드 기반
Deep Canonically Correlated LSTMs
We examine Deep Canonically Correlated LSTMs as a way to learn nonlinear transformations of variable length sequences and embed them into a correlated, fixed dimensional space. We use LSTMs to transform multi-view time-s…
Time SeriesTime Series AnalysisNeural Canonical Transformation with Symplectic Flows
Canonical transformation plays a fundamental role in simplifying and solving classical Hamiltonian systems. We construct flexible and powerful canonical transformations as generative models using symplectic neural networ…
Density EstimationZero-Shot Test-Time Canonicalization using Out-of-Distribution Scoring
Pretrained vision models often misclassify inputs that are rotated, scaled, or sheared, even though these affine transformations leave the object class unchanged. Robustness is usually restored either by building equivar…
Point CloudsTest-Time Canonicalization by Foundation Models for Robust Perception
Real-world visual perception requires invariance to diverse transformations, yet current methods rely heavily on specialized architectures or training on predefined augmentations, limiting generalization. We propose FOCA…
Representation Learning Through Latent Canonicalizations
We seek to learn a representation on a large annotated data source that generalizes to a target domain using limited new supervision. Many prior approaches to this problem have focused on learning "disentangled" represen…
DisentanglementRepresentation Learning