Multi-Task Model Merging via Adaptive Weight Disentanglement

Model merging has gained increasing attention as an efficient and effective technique for integrating task-specific weights from various tasks into a unified multi-task model without retraining or additional data. As a representative approach, Task Arithmetic (TA) has demonstrated that combining task vectors through arithmetic operations facilitates efficient capability transfer between different tasks. In this framework, task vectors are obtained by subtracting the parameter values of a pre-trained model from those of individually fine-tuned models initialized from it. Despite the notable effectiveness of TA, interference among task vectors can adversely affect the performance of the merged model. In this paper, we relax the constraints of Task Arithmetic Property and propose Task Consistency Property, which can be regarded as being free from task interference. Through theoretical derivation, we show that such a property can be approximately achieved by seeking orthogonal task vectors. Guiding by this insight, we propose Adaptive Weight Disentanglement (AWD), which decomposes traditional task vectors into a redundant vector and several disentangled task vectors. The primary optimization objective of AWD is to achieve orthogonality among the disentangled task vectors, thereby closely approximating the desired solution. Notably, these disentangled task vectors can be seamlessly integrated into existing merging methodologies. Experimental results demonstrate that our AWD consistently and significantly improves upon previous merging approaches, achieving state-of-the-art results. Our code is available at \href{https://github.com/FarisXiong/AWD.git}{https://github.com/FarisXiong/AWD.git}.

Kendall's $τ$ Coefficient for Logits Distillation

Knowledge distillation typically employs the Kullback-Leibler (KL) divergence to constrain the student model's output to match the soft labels provided by the teacher model exactly. However, sometimes the optimization direction of the KL divergence loss is not always aligned with the task loss, where a smaller KL divergence could lead to erroneous predictions that diverge from the soft labels. This limitation often results in suboptimal optimization for the student. Moreover, even under temperature scaling, the KL divergence loss function tends to overly focus on the larger-valued channels in the logits, disregarding the rich inter-class information provided by the multitude of smaller-valued channels. This hard constraint proves too challenging for lightweight students, hindering further knowledge distillation. To address this issue, we propose a plug-and-play ranking loss based on Kendall's $\tau$ coefficient, called Rank-Kendall Knowledge Distillation (RKKD). RKKD balances the attention to smaller-valued channels by constraining the order of channel values in student logits, providing more inter-class relational information. The rank constraint on the top-valued channels helps avoid suboptimal traps during optimization. We also discuss different differentiable forms of Kendall's $\tau$ coefficient and demonstrate that the proposed ranking loss function shares a consistent optimization objective with the KL divergence. Extensive experiments on the CIFAR-100 and ImageNet datasets show that our RKKD can enhance the performance of various knowledge distillation baselines and offer broad improvements across multiple teacher-student architecture combinations.

Learn To Learn More Precisely

Meta-learning has been extensively applied in the domains of few-shot learning and fast adaptation, achieving remarkable performance. While Meta-learning methods like Model-Agnostic Meta-Learning (MAML) and its variants provide a good set of initial parameters for the model, the model still tends to learn shortcut features, which leads to poor generalization. In this paper, we propose the formal conception of "learn to learn more precisely", which aims to make the model learn precise target knowledge from data and reduce the effect of noisy knowledge, such as background and noise. To achieve this target, we proposed a simple and effective meta-learning framework named Meta Self-Distillation(MSD) to maximize the consistency of learned knowledge, enhancing the models' ability to learn precise target knowledge. In the inner loop, MSD uses different augmented views of the same support data to update the model respectively. Then in the outer loop, MSD utilizes the same query data to optimize the consistency of learned knowledge, enhancing the model's ability to learn more precisely. Our experiment demonstrates that MSD exhibits remarkable performance in few-shot classification tasks in both standard and augmented scenarios, effectively boosting the accuracy and consistency of knowledge learned by the model.