Search Results for author:
Found 11 papers, 7 papers with code
Scaling Vision Transformers to 22 Billion Parameters
The scaling of Transformers has driven breakthrough capabilities for language models.
Ranked #1 on
Zero-Shot Transfer Image Classification
on ObjectNet
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames
Automatically discovering composable abstractions from raw perceptual data is a long-standing challenge in machine learning.
Teacher-Generated Spatial-Attention Labels Boost Robustness and Accuracy of Contrastive Models
We then traina model with a primary contrastive objective; to this stan-dard configuration, we add a simple output head trained topredict the attentional map for each image, guided by thepseudo labels from teacher model.
SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos
The visual world can be parsimoniously characterized in terms of distinct entities with sparse interactions.
Conditional Object-Centric Learning from Video
Object-centric representations are a promising path toward more systematic generalization by providing flexible abstractions upon which compositional world models can be built.
Addressing the Real-world Class Imbalance Problem in Dermatology
Class imbalance is a common problem in medical diagnosis, causing a standard classifier to be biased towards the common classes and perform poorly on the rare classes.
Revisiting Spatial Invariance with Low-Rank Local Connectivity
Convolutional neural networks are among the most successful architectures in deep learning with this success at least partially attributable to the efficacy of spatial invariance as an inductive bias.
Saccader: Improving Accuracy of Hard Attention Models for Vision
Although deep convolutional neural networks achieve state-of-the-art performance across nearly all image classification tasks, their decisions are difficult to interpret.
Adversarial Reprogramming of Neural Networks
Previous adversarial attacks have been designed to degrade performance of models or cause machine learning models to produce specific outputs chosen ahead of time by the attacker.
Large Margin Deep Networks for Classification
We present a formulation of deep learning that aims at producing a large margin classifier.
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich.
