5 papers
Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations
Yuewei Yang, Hai Li, Yiran Chen
In recent years, discriminative self-supervised methods have made significant strides in advancing various visual tasks. The central idea of learning a data encoder that is robust…
FLSL: Feature-level Self-supervised Learning
Qing Su, Anton Netchaev, Hai Li +1
Current self-supervised learning (SSL) methods (e.g., SimCLR, DINO, VICReg,MOCOv3) target primarily on representations at instance level and do not generalize well to dense predict…
Disentangling Learning Representations with Density Estimation
Eric Yeats, Frank Liu, Hai Li
Disentangled learning representations have promising utility in many applications, but they currently suffer from serious reliability issues. We present Gaussian Channel Autoencode…
NashAE: Disentangling Representations through Adversarial Covariance Minimization
Eric Yeats, Frank Liu, David Womble +1
We present a self-supervised method to disentangle factors of variation in high-dimensional data that does not rely on prior knowledge of the underlying variation profile (e.g., no…
Learning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming
Ximing Qiao, Hai Li
We consider learning and compositionality as the key mechanisms towards simulating human-like intelligence. While each mechanism is successfully achieved by neural networks and sym…