4.4k citations · 6.8k across the 29 of their papers we have counts for
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An Information-Theoretic Approach to Transferability in Task Transfer Learning
Yajie Bao, Yang Li, Shao-Lun Huang +4
Task transfer learning is a popular technique in image processing applications that uses pre-trained models to reduce the supervision cost of related tasks. An important question i…
PALMER: Perception-Action Loop with Memory for Long-Horizon Planning
Onur Beker, Mohammad Mohammadi, Amir Zamir
To achieve autonomy in a priori unknown real-world scenarios, agents should be able to: i) act from high-dimensional sensory observations (e.g., images), ii) learn from past experi…
Task Discovery: Finding the Tasks that Neural Networks Generalize on
Andrei Atanov, Andrei Filatov, Teresa Yeo +2
When developing deep learning models, we usually decide what task we want to solve then search for a model that generalizes well on the task. An intriguing question would be: what…
3D Common Corruptions and Data Augmentation
Oğuzhan Fatih Kar, Teresa Yeo, Andrei Atanov +1
We introduce a set of image transformations that can be used as corruptions to evaluate the robustness of models as well as data augmentation mechanisms for training neural network…
MultiMAE: Multi-modal Multi-task Masked Autoencoders
Roman Bachmann, David Mizrahi, Andrei Atanov +1
We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders (MultiMAE). It differs from standard Masked Autoencoding in two key aspects: I) it can optional…
Simple Control Baselines for Evaluating Transfer Learning
Andrei Atanov, Shijian Xu, Onur Beker +2
Transfer learning has witnessed remarkable progress in recent years, for example, with the introduction of augmentation-based contrastive self-supervised learning methods. While a…
CLIPasso: Semantically-Aware Object Sketching
Yael Vinker, Ehsan Pajouheshgar, Jessica Y. Bo +5
Abstraction is at the heart of sketching due to the simple and minimal nature of line drawings. Abstraction entails identifying the essential visual properties of an object or scen…