24 citations · 92 across the 63 of their papers we have counts for
11 papers · 1 filter
Integral Continual Learning Along the Tangent Vector Field of Tasks
Tian Yu Liu, Aditya Golatkar, Stefano Soatto +1
We propose a lightweight continual learning method which incorporates information from specialized datasets incrementally, by integrating it along the vector field of "generalist"…
Critical Learning Periods for Multisensory Integration in Deep Networks
Michael Kleinman, Alessandro Achille, Stefano Soatto
We show that the ability of a neural network to integrate information from diverse sources hinges critically on being exposed to properly correlated signals during the early phases…
Semi-supervised Vision Transformers at Scale
Zhaowei Cai, Avinash Ravichandran, Paolo Favaro +5
We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architectures to different tasks. To tackle this…
On the Learnability of Physical Concepts: Can a Neural Network Understand What's Real?
Alessandro Achille, Stefano Soatto
We revisit the classic signal-to-symbol barrier in light of the remarkable ability of deep neural networks to generate realistic synthetic data. DeepFakes and spoofing highlight th…
Masked Vision and Language Modeling for Multi-modal Representation Learning
Gukyeong Kwon, Zhaowei Cai, Avinash Ravichandran +3
In this paper, we study how to use masked signal modeling in vision and language (V+L) representation learning. Instead of developing masked language modeling (MLM) and masked imag…
On Leave-One-Out Conditional Mutual Information For Generalization
Mohamad Rida Rammal, Alessandro Achille, Aditya Golatkar +2
We derive information theoretic generalization bounds for supervised learning algorithms based on a new measure of leave-one-out conditional mutual information (loo-CMI). Contrary…
ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training
Yue Zhao, Yantao Shen, Yuanjun Xiong +5
Negative flips are errors introduced in a classification system when a legacy model is updated. Existing methods to reduce the negative flip rate (NFR) either do so at the expense…
X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks
Zhaowei Cai, Gukyeong Kwon, Avinash Ravichandran +4
In this paper, we study the challenging instance-wise vision-language tasks, where the free-form language is required to align with the objects instead of the whole image. To addre…
Class-Incremental Learning with Strong Pre-trained Models
Tz-Ying Wu, Gurumurthy Swaminathan, Zhizhong Li +4
Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes). Instead, we explore an understudied real-world…
MeMOT: Multi-Object Tracking with Memory
Jiarui Cai, Mingze Xu, Wei Li +4
We propose an online tracking algorithm that performs the object detection and data association under a common framework, capable of linking objects after a long time span. This is…
Task Adaptive Parameter Sharing for Multi-Task Learning
Matthew Wallingford, Hao Li, Alessandro Achille +4
Adapting pre-trained models with broad capabilities has become standard practice for learning a wide range of downstream tasks. The typical approach of fine-tuning different models…