8 citations · 30 across the 13 of their papers we have counts for
17 papers
MCNC: Manifold-Constrained Reparameterization for Neural Compression
Chayne Thrash, Ali Abbasi, Reed Andreas +4
The outstanding performance of large foundational models across diverse tasks, from computer vision to speech and natural language processing, has significantly increased their dem…
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…
Backdoor Attacks on Vision Transformers
Akshayvarun Subramanya, Aniruddha Saha, Soroush Abbasi Koohpayegani +2
Vision Transformers (ViT) have recently demonstrated exemplary performance on a variety of vision tasks and are being used as an alternative to CNNs. Their design is based on a sel…
SimA: Simple Softmax-free Attention for Vision Transformers
Soroush Abbasi Koohpayegani, Hamed Pirsiavash
Recently, vision transformers have become very popular. However, deploying them in many applications is computationally expensive partly due to the Softmax layer in the attention b…
PRANC: Pseudo RAndom Networks for Compacting deep models
Parsa Nooralinejad, Ali Abbasi, Soroush Abbasi Koohpayegani +4
We demonstrate that a deep model can be reparametrized as a linear combination of several randomly initialized and frozen deep models in the weight space. During training, we seek…
SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation
K L Navaneet, Soroush Abbasi Koohpayegani, Ajinkya Tejankar +1
Feature regression is a simple way to distill large neural network models to smaller ones. We show that with simple changes to the network architecture, regression can outperform m…
Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning
KL Navaneet, Soroush Abbasi Koohpayegani, Ajinkya Tejankar +3
We are interested in representation learning in self-supervised, supervised, and semi-supervised settings. Some recent self-supervised learning methods like mean-shift (MSF) cluste…
Adaptive Token Sampling For Efficient Vision Transformers
Mohsen Fayyaz, Soroush Abbasi Koohpayegani, Farnoush Rezaei Jafari +5
While state-of-the-art vision transformer models achieve promising results in image classification, they are computationally expensive and require many GFLOPs. Although the GFLOPs…
Constrained Mean Shift for Representation Learning
Ajinkya Tejankar, Soroush Abbasi Koohpayegani, Hamed Pirsiavash
We are interested in representation learning from labeled or unlabeled data. Inspired by recent success of self-supervised learning (SSL), we develop a non-contrastive representati…
Consistent Explanations by Contrastive Learning
Vipin Pillai, Soroush Abbasi Koohpayegani, Ashley Ouligian +2
Post-hoc explanation methods, e.g., Grad-CAM, enable humans to inspect the spatial regions responsible for a particular network decision. However, it is shown that such explanation…
Backdoor Attacks on Self-Supervised Learning
Aniruddha Saha, Ajinkya Tejankar, Soroush Abbasi Koohpayegani +1
Large-scale unlabeled data has spurred recent progress in self-supervised learning methods that learn rich visual representations. State-of-the-art self-supervised methods for lear…
Mean Shift for Self-Supervised Learning
Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash
Most recent self-supervised learning (SSL) algorithms learn features by contrasting between instances of images or by clustering the images and then contrasting between the image c…
ISD: Self-Supervised Learning by Iterative Similarity Distillation
Ajinkya Tejankar, Soroush Abbasi Koohpayegani, Vipin Pillai +2
Recently, contrastive learning has achieved great results in self-supervised learning, where the main idea is to push two augmentations of an image (positive pairs) closer compared…
CompRess: Self-Supervised Learning by Compressing Representations
Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash
Self-supervised learning aims to learn good representations with unlabeled data. Recent works have shown that larger models benefit more from self-supervised learning than smaller…