Diffusion models have demonstrated outstanding performance in generative tasks, making them ideal candidates for image editing. Recent studies highlight their ability to apply desired edits effectively by following textual instructions, yet with two key challenges remaining. First, these models struggle to apply multiple edits simultaneously, resulting in computational inefficiencies due to their reliance on sequential processing. Second, relying on textual prompts to determine the editing region can lead to unintended alterations to the image. We introduce FunEditor, an efficient diffusion model designed to learn atomic editing functions and perform complex edits by aggregating simpler functions. This approach enables complex editing tasks, such as object movement, by aggregating multiple functions and applying them simultaneously to specific areas. Our experiments demonstrate that FunEditor significantly outperforms recent inference-time optimization methods and fine-tuned models, either quantitatively across various metrics or through visual comparisons or both, on complex tasks like object movement and object pasting. In the meantime, with only 4 steps of inference, FunEditor achieves 5--24 times inference speedups over existing popular methods.

Hyperspectral images (HSIs) offer great potential for computational pathology. But, limited by the lack of adequate annotated data and the high spectral redundancy of HSIs, traditional supervised learning techniques are usually bottlenecked. To exploit the structural properties of HSIs and learn representations with good transferability, we propose Separated Self-Supervised Spectral Regression (S4R). Concretely, we find one spectral band can be represented by a linear combination of the remaining bands. Regressing the distribution of the linear coefficients learns the inherent properties of HSIs and pathological information about the tissue. Besides, reconstructing the missing band, especially the tissue boundaries makes the model learn pathology details that are critical to downstream tasks. Coupling these two pretext tasks makes the self-supervised model understand spectral structures of HSIs w.r.t. pathological semantics and spatial micro details. Furthermore, we design two brand-new architectures to avoid the interference of extraneous signal based on S4R: S4R-CLS and S4R-SEG for HSI classification and segmentation, respectively. Two downstream tasks are incorporated into a unified framework, which first encodes different bands from HSIs via a depthwise separable encoder, and then selectively aggregates band features to generate final predictions. In S4R-SEG, we propose to pick the best matching bands with the guidance of a classification paradigm. Extensive experiments show S4R performs much better than competitors on both tasks. Theoretical analysis and clinical discussion also indicate the great potential for further medical applications. The code and pre-trained checkpoints are available at https://github.com/DeepMed-Lab-ECNU/S4R.

Neural Architecture Search is a costly practice. The fact that a search space can span a vast number of design choices with each architecture evaluation taking nontrivial overhead makes it hard for an algorithm to sufficiently explore candidate networks. In this paper, we propose Auto-Build, a scheme which learns to align the latent embeddings of operations and architecture modules with the ground-truth performance of the architectures they appear in. By doing so, AutoBuild is capable of assigning interpretable importance scores to architecture modules, such as individual operation features and larger macro operation sequences such that high-performance neural networks can be constructed without any need for search. Through experiments performed on state-of-the-art image classification, segmentation, and Stable Diffusion models, we show that by mining a relatively small set of evaluated architectures, AutoBuild can learn to build high-quality architectures directly or help to reduce search space to focus on relevant areas, finding better architectures that outperform both the original labeled ones and ones found by search baselines. Code available at https://github.com/Ascend-Research/AutoBuild

Specialized hardware has become an indispensable component to deep neural network (DNN) acceleration. To keep up with the rapid evolution of neural networks, holistic and automated solutions for jointly optimizing both hardware (HW) architectures and software (SW) mapping have been studied. These studies face two major challenges. First, the combined HW-SW design space is vast, which hinders the finding of optimal or near-optimal designs. This issue is exacerbated for industrial cases when cycle accurate models are used for design evaluation in the joint optimization. Second, HW design is prone to overfitting to the input DNNs used in the HW-SW co-optimization. To address these issues, in this paper, we propose UNICO, an efficient Unified Co-Optimization framework with a novel Robustness metric for better HW generalization. Guided by a high-fidelity surrogate model, UNICO employs multi-objective Bayesian optimization to effectively explore the HW design space, and conducts adaptive, parallel and scalable software mapping search based on successive halving. To reduce HW overfitting, we propose a HW robustness metric by relating a HW configuration’s quality to its sensitivity in software mapping search, and quantitatively incorporate this metric to search for more robust HW design(s). We implement UNICO in open source accelerator platform, and compare it with the state-of-the-art solution HASCO. Experiments show that UNICO significantly outperforms HASCO; it finds design(s) with similar quality to HASCO up to 4 × faster, and eventually converges to better and more robust designs. Finally, we deploy UNICO for optimizing an industrial accelerator, and show that it generates enhanced HW design(s) for key real-world DNNs.

Optimizing Deep Neural Networks (DNNs) to obtain high-quality models for efficient real-world deployment has posed multi-faceted challenges to machine learning engineers. Existing methods either search for neural architectures in heuristic design spaces or apply low-level adjustments to computation primitives to improve inference efficiency on hardware. We present Automated Graph Optimization (AutoGO), a framework to evolve neural networks in a low-level Computation Graph (CG) of primitive operations to improve both its performance and hardware friendliness. Through a tokenization scheme, AutoGO performs variable-sized segment mutations, making both primitive changes and larger-grained changes to CGs. We introduce our segmentation and mutation algorithms, efficient frequent segment mining technique, as well as a pretrained context-aware predictor to estimate the impact of segment replacements. Extensive experimental results show that AutoGO can automatically evolve several typical large convolutional networks to achieve significant task performance improvement and FLOPs reduction on a range of CV tasks, ranging from Classification, Semantic Segmentation, Human Pose Estimation, to Super Resolution, yet without introducing any newer primitive operations. We also demonstrate the lightweight deployment results of AutoGO-optimized super-resolution and denoising U-Nets on a cycle simulator for a Neural Processing Unit (NPU), achieving PSNR improvement and latency/power reduction simultaneously. Code available at https://github.com/Ascend-Research/AutoGO.

Firstly, this paper presents a novel hierarchical classification feature extraction technology for moving target detection using radar echo. Secondly, a progress is made to acquire the envelope and Doppler feature in the MATLAB simulation analysis. Finally, an efficient combination of the empirical mode decomposition (EMD) with the all-phase FFT (apFFT) to implement the hierarchical classification feature extraction of the moving target. The computation used by this technology is very simple and efficient and needs much less memory resource and should be perfectly adequate for computation in real time.