2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2026
BRo-JEPA: Learning Modular Transformations in Latent Space
Divyansh Jha, Yuanfang Xie, Brennen Yu +1
Can neural networks learn algebraic rules from visual inputs, or do they merely fit observed patterns? We study this question using MNIST (or EMNIST letters) as states and modular…
cs.CV2024★ 2 cited
AI Art Neural Constellation: Revealing the Collective and Contrastive State of AI-Generated and Human Art
Faizan Farooq Khan, Diana Kim, Divyansh Jha +5
Discovering the creative potentials of a random signal to various artistic expressions in aesthetic and conceptual richness is a ground for the recent success of generative machine…
cs.CV2021
Imaginative Walks: Generative Random Walk Deviation Loss for Improved Unseen Learning Representation
Divyansh Jha, Kai Yi, Ivan Skorokhodov +1
We propose a novel loss for generative models, dubbed as GRaWD (Generative Random Walk Deviation), to improve learning representations of unexplored visual spaces. Quality learning…