1 code implementation • 14 Nov 2024 • Alexander C. Li, Yuandong Tian, Beidi Chen, Deepak Pathak, Xinlei Chen
We show this by introducing a simple method called attention transfer, where only the attention patterns from a pre-trained teacher ViT are transferred to a student, either by copying or distilling the attention maps.
no code implementations • 27 May 2024 • Florian Bordes, Richard Yuanzhe Pang, Anurag Ajay, Alexander C. Li, Adrien Bardes, Suzanne Petryk, Oscar Mañas, Zhiqiu Lin, Anas Mahmoud, Bargav Jayaraman, Mark Ibrahim, Melissa Hall, Yunyang Xiong, Jonathan Lebensold, Candace Ross, Srihari Jayakumar, Chuan Guo, Diane Bouchacourt, Haider Al-Tahan, Karthik Padthe, Vasu Sharma, Hu Xu, Xiaoqing Ellen Tan, Megan Richards, Samuel Lavoie, Pietro Astolfi, Reyhane Askari Hemmat, Jun Chen, Kushal Tirumala, Rim Assouel, Mazda Moayeri, Arjang Talattof, Kamalika Chaudhuri, Zechun Liu, Xilun Chen, Quentin Garrido, Karen Ullrich, Aishwarya Agrawal, Kate Saenko, Asli Celikyilmaz, Vikas Chandra
Then, we present and discuss approaches to evaluate VLMs.
1 code implementation • 27 Nov 2023 • Mihir Prabhudesai, Tsung-Wei Ke, Alexander C. Li, Deepak Pathak, Katerina Fragkiadaki
Our method, Diffusion-TTA, adapts pre-trained discriminative models such as image classifiers, segmenters and depth predictors, to each unlabelled example in the test set using generative feedback from a diffusion model.
3 code implementations • ICCV 2023 • Alexander C. Li, Mihir Prabhudesai, Shivam Duggal, Ellis Brown, Deepak Pathak
Our generative approach to classification, which we call Diffusion Classifier, attains strong results on a variety of benchmarks and outperforms alternative methods of extracting knowledge from diffusion models.
Ranked #1 on
Image Classification
on ObjectNet (ImageNet classes)
1 code implementation • 27 Feb 2023 • Alexander C. Li, Ellis Brown, Alexei A. Efros, Deepak Pathak
Modern vision models typically rely on fine-tuning general-purpose models pre-trained on large, static datasets.
1 code implementation • 29 Sep 2022 • Alexander C. Li, Alexei A. Efros, Deepak Pathak
We empirically analyze these non-contrastive methods and find that SimSiam is extraordinarily sensitive to dataset and model size.
1 code implementation • NeurIPS 2021 • Alexander C. Li, Deepak Pathak
We propose a simple architecture for deep reinforcement learning by embedding inputs into a learned Fourier basis and show that it improves the sample efficiency of both state-based and image-based RL.
no code implementations • NeurIPS 2020 • Alexander C. Li, Lerrel Pinto, Pieter Abbeel
Compared to standard relabeling techniques, Generalized Hindsight provides a substantially more efficient reuse of samples, which we empirically demonstrate on a suite of multi-task navigation and manipulation tasks.
no code implementations • 17 Oct 2019 • Murtaza Dalal, Alexander C. Li, Rohan Taori
Autoregressive (AR) models have become a popular tool for unsupervised learning, achieving state-of-the-art log likelihood estimates.
no code implementations • ICLR 2020 • Alexander C. Li, Carlos Florensa, Ignasi Clavera, Pieter Abbeel
Hierarchical reinforcement learning is a promising approach to tackle long-horizon decision-making problems with sparse rewards.