Monte Carlo Tree Search

To defend deep neural networks from adversarial attacks adversarial training has been drawing increasing attention for its effectiveness. However the accuracy and robustness resulting from the adversarial training are limited by the architecture because adversarial training improves accuracy and robustness by adjusting the weight connection affiliated to the architecture. In this work we propose ARNAS to search for accurate and robust architectures for adversarial training. First we design an accurate and robust search space in which the placement of the cells and the proportional relationship of the filter numbers are carefully determined. With the design the architectures can obtain both accuracy and robustness by deploying accurate and robust structures to their sensitive positions respectively. Then we propose a differentiable multi-objective search strategy performing gradient descent towards directions that are beneficial for both natural loss and adversarial loss thus the accuracy and robustness can be guaranteed at the same time. We conduct comprehensive experiments in terms of white-box attacks black-box attacks and transferability. Experimental results show that the searched architecture has the strongest robustness with the competitive accuracy and breaks the traditional idea that NAS-based architectures cannot transfer well to complex tasks in robustness scenarios. By analyzing outstanding architectures searched we also conclude that accurate and robust neural architectures tend to deploy different structures near the input and output which has great practical significance on both hand-crafting and automatically designing of accurate and robust architectures.

While AI-generated content has garnered significant attention achieving photo-realistic video synthesis remains a formidable challenge. Despite the promising advances in diffusion models for video generation quality the complex model architecture and substantial computational demands for both training and inference create a significant gap between these models and real-world applications. This paper presents SNED a superposition network architecture search method for efficient video diffusion model. Our method employs a supernet training paradigm that targets various model cost and resolution options using a weight-sharing method. Moreover we propose the supernet training sampling warm-up for fast training optimization. To showcase the flexibility of our method we conduct experiments involving both pixel-space and latent-space video diffusion models. The results demonstrate that our framework consistently produces comparable results across different model options with high efficiency. According to the experiment for the pixel-space video diffusion model we can achieve consistent video generation results simultaneously across 64 x 64 to 256 x 256 resolutions with a large range of model sizes from 640M to 1.6B number of parameters for pixel-space video diffusion models.

Existing tracking methods mainly focus on learning better target representation or developing more robust prediction models to improve tracking performance. While tracking performance has significantly improved the target loss issue occurs frequently due to tracking failures complete occlusion or out-of-view situations. However considerably less attention is paid to the self-recovery issue of tracking methods which is crucial for practical applications. To this end we propose a recoverable tracking framework \ourmethod that uses a tree-structured memory to dynamically associate a tracker and a detector to enable self-recovery ability. Specifically we propose a Positive-Negative Tree-structured memory to chronologically store and maintain positive and negative target samples. Upon the PN tree memory we develop corresponding walking rules for determining the state of the target and define a set of control flows to unite the tracker and the detector in different tracking scenarios. Our core idea is to use the support samples of positive and negative target categories to establish a relative distance-based criterion for a reliable assessment of target loss. The favorable performance in comparison against the state-of-the-art methods on numerous challenging benchmarks demonstrates the effectiveness of the proposed algorithm. All the source code and trained models will be released at https://github.com/NorahGreen/RTracker.

2D keypoints are commonly used as an additional cue to refine estimated 3D human meshes. Current methods optimize the pose and shape parameters with a reprojection loss on the provided 2D keypoints. Such an approach while simple and intuitive has limited effectiveness because the optimal solution is hard to find in ambiguous parameter space and may sacrifice depth. Additionally divergent gradients from distal joints complicate and deviate the refinement of proximal joints in the kinematic chain. To address these we introduce Kinematic-Tree Rotation (KITRO) a novel mesh refinement strategy that explicitly models depth and human kinematic-tree structure. KITRO treats refinement from a bone-wise perspective. Unlike previous methods which perform gradient-based optimizations our method calculates bone directions in closed form. By accounting for the 2D pose bone length and parent joint's depth the calculation results in two possible directions for each child joint. We then use a decision tree to trace binary choices for all bones along the human skeleton's kinematic-tree to select the most probable hypothesis. Our experiments across various datasets and baseline models demonstrate that KITRO significantly improves 3D joint estimation accuracy and achieves an ideal 2D fit simultaneously. Our code available at: https://github.com/MartaYang/KITRO.

The boundless possibility of neural networks which can be used to solve a problem - each with different performance - leads to a situation where a Deep Learning expert is required to identify the best neural network. This goes against the hope of removing the need for experts. Neural Architecture Search (NAS) offers a solution to this by automatically identifying the best architecture. However to date NAS work has focused on a small set of datasets which we argue are not representative of real-world problems. We introduce eight new datasets created for a series of NAS Challenges: AddNIST Language MultNIST CIFARTile Gutenberg Isabella GeoClassing and Chesseract. These datasets and challenges are developed to direct attention to issues in NAS development and to encourage authors to consider how their models will perform on datasets unknown to them at development time. We present experimentation using standard Deep Learning methods as well as the best results from challenge participants

Neural field is an emerging paradigm in data representation that trains a neural network to approximate the given signal. A key obstacle that prevents its widespread adoption is the encoding speed---generating neural fields requires an overfitting of a neural network which can take a significant number of SGD steps to reach the desired fidelity level. In this paper we delve into the impacts of data transformations on the speed of neural field training specifically focusing on how permuting pixel locations affect the convergence speed of SGD. Counterintuitively we find that randomly permuting the pixel locations can considerably accelerate the training. To explain this phenomenon we examine the neural field training through the lens of PSNR curves loss landscapes and error patterns. Our analyses suggest that the random pixel permutations remove the easy-to-fit patterns which facilitate easy optimization in the early stage but hinder capturing fine details of the signal.

We propose a hierarchical correlation clustering method that extends the well-known correlation clustering to produce hierarchical clusters applicable to both positive and negative pairwise dissimilarities. Then in the following we study unsupervised representation learning with such hierarchical correlation clustering. For this purpose we first investigate embedding the respective hierarchy to be used for tree preserving embedding and feature extraction. Thereafter we study the extension of minimax distance measures to correlation clustering as another representation learning paradigm. Finally we demonstrate the performance of our methods on several datasets.

Supernet is a core component in many recent Neural Architecture Search (NAS) methods. It not only helps embody the search space but also provides a (relative) estimation of the final performance of candidate architectures. Thus it is critical that the top architectures ranked by a supernet should be consistent with those ranked by true performance which is known as the order-preserving ability. In this work we analyze the order-preserving ability on the whole search space (global) and a sub-space of top architectures (local) and empirically show that the local order-preserving for current two-stage NAS methods still need to be improved. To rectify this we propose a novel concept of Supernet Shifting a refined search strategy combining architecture searching with supernet fine-tuning. Specifically apart from evaluating the training loss is also accumulated in searching and the supernet is updated every iteration. Since superior architectures are sampled more frequently in evolutionary searching the supernet is encouraged to focus on top architectures thus improving local order-preserving. Besides a pre-trained supernet is often un-reusable for one-shot methods. We show that Supernet Shifting can fulfill transferring supernet to a new dataset. Specifically the last classifier layer will be unset and trained through evolutionary searching. Comprehensive experiments show that our method has better order-preserving ability and can find a dominating architecture. Moreover the pre-trained supernet can be easily transferred into a new dataset with no loss of performance.

We present an approach to accelerate Neural Field training by efficiently selecting sampling locations. While Neural Fields have recently become popular it is often trained by uniformly sampling the training domain or through handcrafted heuristics. We show that improved convergence and final training quality can be achieved by a soft mining technique based on importance sampling: rather than either considering or ignoring a pixel completely we weigh the corresponding loss by a scalar. To implement our idea we use Langevin Monte-Carlo sampling. We show that by doing so regions with higher error are being selected more frequently leading to more than 2x improvement in convergence speed. The code and related resources for this study are publicly available at https://ubc-vision.github.io/nf-soft-mining/.

Training-free network architecture search (NAS) aims to discover high-performing networks with zero-cost proxies capturing network characteristics related to the final performance. However network rankings estimated by previous training-free NAS methods have shown weak correlations with the performance. To address this issue we propose AZ-NAS a novel approach that leverages the ensemble of various zero-cost proxies to enhance the correlation between a predicted ranking of networks and the ground truth substantially in terms of the performance. To achieve this we introduce four novel zero-cost proxies that are complementary to each other analyzing distinct traits of architectures in the views of expressivity progressivity trainability and complexity. The proxy scores can be obtained simultaneously within a single forward and backward pass making an overall NAS process highly efficient. In order to integrate the rankings predicted by our proxies effectively we introduce a non-linear ranking aggregation method that highlights the networks highly-ranked consistently across all the proxies. Experimental results conclusively demonstrate the efficacy and efficiency of AZ-NAS outperforming state-of-the-art methods on standard benchmarks all while maintaining a reasonable runtime cost.