8 citations · 30 across the 13 of their papers we have counts for
4 papers · 1 filter
GeNIe: Generative Hard Negative Images Through Diffusion
Soroush Abbasi Koohpayegani, Anuj Singh, K L Navaneet +2
Data augmentation is crucial in training deep models, preventing them from overfitting to limited data. Recent advances in generative AI, e.g., diffusion models, have enabled more…
CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization
KL Navaneet, Kossar Pourahmadi Meibodi, Soroush Abbasi Koohpayegani +1
3D Gaussian Splatting (3DGS) is a new method for modeling and rendering 3D radiance fields that achieves much faster learning and rendering time compared to SOTA NeRF methods. Howe…
SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers
KL Navaneet, Soroush Abbasi Koohpayegani, Essam Sleiman +1
Recently, there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of de…
NOLA: Compressing LoRA using Linear Combination of Random Basis
Soroush Abbasi Koohpayegani, KL Navaneet, Parsa Nooralinejad +2
Fine-tuning Large Language Models (LLMs) and storing them for each downstream task or domain is impractical because of the massive model size (e.g., 350GB in GPT-3). Current litera…