CLIP model is an Efficient Continual Learner (2210.03114v1)

Abstract: The continual learning setting aims to learn new tasks over time without forgetting the previous ones. The literature reports several significant efforts to tackle this problem with limited or no access to previous task data. Among such efforts, typical solutions offer sophisticated techniques involving memory replay, knowledge distillation, model regularization, and dynamic network expansion. The resulting methods have a retraining cost at each learning task, dedicated memory requirements, and setting-specific design choices. In this work, we show that a frozen CLIP (Contrastive Language-Image Pretraining) model offers astounding continual learning performance without any fine-tuning (zero-shot evaluation). We evaluate CLIP under a variety of settings including class-incremental, domain-incremental and task-agnostic incremental learning on five popular benchmarks (ImageNet-100 & 1K, CORe50, CIFAR-100, and TinyImageNet). Without any bells and whistles, the CLIP model outperforms the state-of-the-art continual learning approaches in the majority of the settings. We show the effect on the CLIP model's performance by varying text inputs with simple prompt templates. To the best of our knowledge, this is the first work to report the CLIP zero-shot performance in a continual setting. We advocate the use of this strong yet embarrassingly simple baseline for future comparisons in the continual learning tasks.

CLIP Model as an Efficient Continual Learner

Continual learning (CL) refers to a machine learning paradigm aimed at enabling models to learn new tasks sequentially without the necessity of forgetting or erasing previously acquired knowledge. Existing literature in CL delineates an arsenal of techniques devised to address the problem of catastrophic forgetting, notable among them being memory replay, knowledge distillation, model regularization, and dynamic network expansion. However, these methods demand constant retraining, incur high computational costs, and are often limited by dedicated memory requirements.

Amidst the quest for models adept in continual learning, Thengane et al. propose an innovative approach leveraging the Contrastive Language-Image Pretraining (CLIP) model. The authors assert that a frozen CLIP model, applied in a zero-shot evaluation context, exhibits exceptional performance across various continual learning settings, outperforming state-of-the-art approaches. The CLIP model's success in this framework introduces a significant proposition; it functions efficiently without necessitating any fine-tuning or parameter adjustment.

The paper conducts rigorous evaluations of CLIP's performance across a gamut of settings: class-incremental, domain-incremental, and task-agnostic incremental learning on prevalent benchmarks such as ImageNet-100 {content} 1K, CORe50, CIFAR-100, and TinyImageNet. Results establish CLIP’s superiority and substantial robustness in these paradigms. Notably, in class-incremental experiments on datasets such as CIFAR-100 and ImageNet, CLIP notably surpasses existing methods, attaining superior last and average accuracy scores. Remarkably, the CLIP model delivers efficient continual learning without expanding its architecture, employing memory buffers, or requiring hyperparameter optimizations.

In a domain-incremental setting, the paper compares CLIP's performance with benchmarks from recent competitions, such as the CVPR 2022 Continual LEArning on Real Imagery (CLEAR) Challenge. The empirical results underscore CLIP's competitive, sometimes superior, performance in both forward and backward transfer metrics. Moreover, CLIP demonstrates prowess in task-agnostic settings, where conventional CL methods often falter, offering a higher test accuracy with a simplistic application approach devoid of training or task identity knowledge.

Addressing the influence of prompts, the paper explores the effect of varied class names and prompt engineering on the model's accuracy, indicating that refined prompt strategies can further enhance CLIP’s continual learning performance. By evaluating different textual class names and prompt templates, the work offers insights into the nuanced impacts of textual inputs on the model's predictive accuracy in continual learning environments.

The implications of these findings are significant on both practical and theoretical fronts. Practically, they suggest the possibility of replacing complex, resource-intensive continual learning strategies with a straightforward, generalizable approach built on CLIP’s capabilities. Theoretically, the insights derived from CLIP’s performance in autonomous continual learning paradigms could inform future explorations into foundational models capable of incremental adaptations without retraining or hyperparameter complexities.

In conclusion, the paper by Thengane et al. underscores the potential of CLIP as a robust continual learner across diverse settings, presenting it as a formidable baseline for future comparisons. The streamlined deployment of CLIP, without retraining or memory requisites, could redefine prevailing methodologies in continual learning, fostering advancements in adaptive artificial intelligence systems that transcend traditional boundaries.