Speculative decoding has emerged as a promising technique to accelerate large language model inference by employing a smaller draft model to predict multiple tokens, which are then verified in parallel by the larger target model.However, existing approaches face a fundamental limitation: candidates at the same tree layer share identical feature representations, constraining diversity and diminishing overall effectiveness.We identify this as an intra-layer coupling problem that limits prediction accuracy.To address this challenge, we propose Jakiro, which introduces decoupled Mixture of Experts (MoE) into the draft model, enabling different experts to generate diverse candidate tokens from distinct feature spaces.We further propose Contrastive-Enhanced Parallel Decoding (CEPD) that combines autoregressive and parallel decoding with a contrastive mechanism to reduce inference steps while maintaining accuracy.Extensive experiments across diverse models and tasks demonstrate that Jakiro achieves significant speedups over strong baselines, with particularly notable improvements in non-greedy decoding scenarios where token diversity is crucial.

The key point of face recognition is creating a discriminative feature representation to ensure intraclass compactness and interclass separability. Softmax loss is widely used in deep learning networks, but it is indirect for face verification. Center loss is effective to improve intraclass compactness, while interclass distances are ignored. In this letter, we propose a novel loss function, termed margin loss, to enlarge distances of interclass and reduce intraclass variations simultaneously. Margin loss aims to focus on samples hard to classify by a distance margin. Different from Softmax loss, margin loss is based on Euclidean distances that can directly measure face similarity. Experiments on different datasets have demonstrated the effectiveness of our method.

Face images captured by surveillance videos usually have limited resolution. Due to resolution mismatch, it is hard to match high-resolution (HR) faces with low-resolution (LR) faces directly. Recently, multidimensional scaling (MDS) has been employed to solve the problem. In this letter, we proposed a more discriminative MDS method to learn a mapping matrix, which projects the HR images and LR images to a common subspace. Our method is discriminative since both interclass distances and intraclass distances are taken into consideration. We add an interclass constraint to enlarge the distances of different subjects in the subspace to ensure discriminability. Besides, we consider not only the relationship of HR-LR images, but also the relationship of HR-HR images and LR-LR images in order to preserve local consistency. Experimental results on FERET, Multi-PIE, and SCface databases demonstrate the effectiveness of our proposed approach.