We consider the problem of training a deep neural network on a given classification task, e.g., ImageNet-1K (IN1K), so that it excels at both the training task as well as at other (future) transfer tasks. These two seemingly contradictory properties impose a trade-off between improving the model’s generalization and maintaining its performance on the original task. Models trained with self-supervised learning tend to generalize better than their supervised counterparts for transfer learning; yet, they still lag behind supervised models on IN1K. In this paper, we propose a supervised learning setup that leverages the best of both worlds. We extensively analyze supervised training using multi-scale crops for data augmentation and an expendable projector head, and reveal that the design of the projector allows us to control the trade-off between performance on the training task and transferability. We further replace the last layer of class weights with class prototypes computed on the fly using a memory bank and derive two models: t-ReX that achieves a new state of the art for transfer learning and outperforms top methods such as DINO and PAWS on IN1K, and t-ReX* that matches the highly optimized RSB-A1 model on IN1K while performing better on transfer tasks.
No reason for no supervision: improved generalization in supervised models
| Mert Bulent Sariyildiz, Yannis Kalantidis, Karteek Alahari, Diane Larlus |
| International Conference on Learning Representations (ICLR), Kigali, Rwanda, 1–5 May, 2023 |
ImageNet-1K (IN1K) vs transfer task performance for ResNet50. We report IN1K (Top-1 accuracy) and transfer performance (log odds) averaged over 13 datasets (the 5 ImageNet-CoG concept generalization datasets, Aircraft, Cars196, DTD, EuroSAT, Flowers, Pets, Food101 and SUN397) for a large number of our models trained with the supervised training setup we propose. Models on the convex hull are denoted by stars. We compare to public state-of-the-art (SotA) models: the supervised RSB-A1 and SupCon models, the self- supervised DINO, the semi-supervised PAWS and a variant of LOOK using multi-crop.
@inproceedings{sariyildiz2023improving, title={No Reason for No Supervision: Improved Generalization in Supervised Models}, author={Sariyildiz, Mert Bulent and Kalantidis, Yannis and Alahari, Karteek and Larlus, Diane}, booktitle={International Conference on Learning Representations}, year={2023} }
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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