377 citations · 860 across the 18 of their papers we have counts for
8 papers · 1 filter
TILDE: TILt-based Distributional Erasure for Concept Unlearning
Naveen George, Naoki Murata, Yuhta Takida +2
Concept unlearning in text-to-image diffusion models is critical for safe and practical deployment: with rising privacy concerns, copyright disputes, trademark constraints, and saf…
Rethinking Dataset Distillation: Hard Truths about Soft Labels
Priyam Dey, Aditya Sahdev, Sunny Bhati +2
Despite the perceived success of large-scale dataset distillation (DD) methods, recent evidence finds that simple random image baselines perform on-par with state-of-theart DD meth…
Locality-Aware Continual Unlearning for Diffusion Models
Naveen George, Naoki Murata, Yuhta Takida +2
Real-world deployment of text-to-image diffusion models requires continual concept removal as new privacy, copyright, or safety obligations arise over time. Existing unlearning met…
Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation
Gaurav Patel, Konda Reddy Mopuri, Qiang Qiu
Data-free Knowledge Distillation (DFKD) has gained popularity recently, with the fundamental idea of carrying out knowledge transfer from a Teacher neural network to a Student neur…
Class Balancing GAN with a Classifier in the Loop
Harsh Rangwani, Konda Reddy Mopuri, R. Venkatesh Babu
Generative Adversarial Networks (GANs) have swiftly evolved to imitate increasingly complex image distributions. However, majority of the developments focus on performance of GANs…
Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation
Gaurav Kumar Nayak, Konda Reddy Mopuri, Anirban Chakraborty
Knowledge Distillation is an effective method to transfer the learning across deep neural networks. Typically, the dataset originally used for training the Teacher model is chosen…
iDLG: Improved Deep Leakage from Gradients
Bo Zhao, Konda Reddy Mopuri, Hakan Bilen
It is widely believed that sharing gradients will not leak private training data in distributed learning systems such as Collaborative Learning and Federated Learning, etc. Recentl…
Zero-Shot Knowledge Distillation in Deep Networks
Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj +2
Knowledge distillation deals with the problem of training a smaller model (Student) from a high capacity source model (Teacher) so as to retain most of its performance. Existing ap…