377 citations · 579 across the 7 of their papers we have counts for
12 papers
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…
Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data
Gaurav Kumar Nayak, Konda Reddy Mopuri, Saksham Jain +1
Pretrained deep models hold their learnt knowledge in the form of model parameters. These parameters act as "memory" for the trained models and help them generalize well on unseen…
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…
Dataset Condensation with Gradient Matching
Bo Zhao, Konda Reddy Mopuri, Hakan Bilen
As the state-of-the-art machine learning methods in many fields rely on larger datasets, storing datasets and training models on them become significantly more expensive. This pape…
Adversarial Fooling Beyond "Flipping the Label"
Konda Reddy Mopuri, Vaisakh Shaj, R. Venkatesh Babu
Recent advancements in CNNs have shown remarkable achievements in various CV/AI applications. Though CNNs show near human or better than human performance in many critical tasks, t…
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…