5 papers
The Easy Path to Robustness: Coreset Selection using Sample Hardness
Pranav Ramesh, Arjun Roy, Deepak Ravikumar +2
Designing adversarially robust models from a data-centric perspective requires understanding which input samples are most crucial for learning resilient features. While coreset sel…
Coresets from Trajectories: Selecting Data via Correlation of Loss Differences
Manish Nagaraj, Deepak Ravikumar, Kaushik Roy
Deep learning models achieve state-of-the-art performance across domains but face scalability challenges in real-time or resource-constrained scenarios. To address this, we propose…
Towards Scalable Modeling of Compressed Videos for Efficient Action Recognition
Shristi Das Biswas, Efstathia Soufleri, Arani Roy +1
Training robust deep video representations has proven to be computationally challenging due to substantial decoding overheads, the enormous size of raw video streams, and their inh…
Finding the Muses: Identifying Coresets through Loss Trajectories
Manish Nagaraj, Deepak Ravikumar, Efstathia Soufleri +1
Deep learning models achieve state-of-the-art performance across domains but face scalability challenges in real-time or resource-constrained scenarios. To address this, we propose…
PIXELS: Progressive Image Xemplar-based Editing with Latent Surgery
Shristi Das Biswas, Matthew Shreve, Xuelu Li +2
Recent advancements in language-guided diffusion models for image editing are often bottle-necked by cumbersome prompt engineering to precisely articulate desired changes. An intui…