2 citations · 2 across the 8 of their papers we have counts for
7 papers · 1 filter
Harmonizing Geometry and Uncertainty: Diffusion with Hyperspheres
Muskan Dosi, Chiranjeev Chiranjeev, Kartik Thakral +2
Do contemporary diffusion models preserve the class geometry of hyperspherical data? Standard diffusion models rely on isotropic Gaussian noise in the forward process, inherently f…
LitMAS: A Lightweight and Generalized Multi-Modal Anti-Spoofing Framework for Biometric Security
Nidheesh Gorthi, Kartik Thakral, Rishabh Ranjan +2
Biometric authentication systems are increasingly being deployed in critical applications, but they remain susceptible to spoofing. Since most of the research efforts focus on moda…
Fine-Grained Erasure in Text-to-Image Diffusion-based Foundation Models
Kartik Thakral, Tamar Glaser, Tal Hassner +2
Existing unlearning algorithms in text-to-image generative models often fail to preserve the knowledge of semantically related concepts when removing specific target concepts: a ch…
Continual Unlearning for Foundational Text-to-Image Models without Generalization Erosion
Kartik Thakral, Tamar Glaser, Tal Hassner +2
How can we effectively unlearn selected concepts from pre-trained generative foundation models without resorting to extensive retraining? This research introduces `continual unlear…
HyperSpaceX: Radial and Angular Exploration of HyperSpherical Dimensions
Chiranjeev Chiranjeev, Muskan Dosi, Kartik Thakral +2
Traditional deep learning models rely on methods such as softmax cross-entropy and ArcFace loss for tasks like classification and face recognition. These methods mainly explore ang…
Are Face Detection Models Biased?
Surbhi Mittal, Kartik Thakral, Puspita Majumdar +2
The presence of bias in deep models leads to unfair outcomes for certain demographic subgroups. Research in bias focuses primarily on facial recognition and attribute prediction wi…