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Human has the remarkable ability of learning novel objects by browsing extremely few examples, which may be attributed to the generic and robust feature extracted in the ventral stream of our brain for representing visual objects. In this sense, the tuning characteristics of ventral stream&#39;s neurons can be useful prior knowledge to improve few-shot classification. Specifically, we computationally model two groups of neurons found in ventral stream which are respectively sensitive to shape cues and color cues. Then we propose the hierarchical feature regularization method with these neuron models to regularize the backbone of a few-shot model, thus making it producing more generic and robust features for few-shot classification. In addition, to simulate the tuning characteristic that neuron firing at a higher rate in response to foreground stimulus elements compared to background elements, which we call belongingness, we design a foreground segmentation algorithm based on the observation that the foreground object usually does not appear at the edge of the picture, then multiply the foreground mask with the backbone of few-shot model. Our method is model-agnostic and can be applied to few-shot models with different backbones, training paradigms and classifiers.

AAAI 2023

Exploring Tuning Characteristics of Ventral Stream&#39;s Neurons for Few-Shot Image Classification

feature regularization

brain-inspired artificial intelligence

few-shot learning

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
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The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

Human has the remarkable ability of learning novel objects by browsing extremely few examples, which may be attributed to the generic and robust feature extracted in the ventral stream of our brain for representing visual objects. In this sense, the tuning characteristics of ventral stream's neurons can be useful prior knowledge to improve few-shot classification. Specifically, we computationally model two groups of neurons found in ventral stream which are respectively sensitive to shape cues and color cues. Then we propose the hierarchical feature regularization method with these neuron models to regularize the backbone of a few-shot model, thus making it producing more generic and robust features for few-shot classification. In addition, to simulate the tuning characteristic that neuron firing at a higher rate in response to foreground stimulus elements compared to background elements, which we call belongingness, we design a foreground segmentation algorithm based on the observation that the foreground object usually does not appear at the edge of the picture, then multiply the foreground mask with the backbone of few-shot model. Our method is model-agnostic and can be applied to few-shot models with different backbones, training paradigms and classifiers.

Exploring Tuning Characteristics of Ventral Stream's Neurons for Few-Shot Image Classification

poster

Pruning has been an effective solution to reduce the number of computations and the memory requirement in deep learning.
The pruning unit plays an important role in exploiting the GPU resources efficiently. 
The filter is proposed as a simple pruning unit of structured pruning.
However, since the filter is quite large as pruning unit, the accuracy drop is considerable with a high pruning ratio.
GPU rearranges the weight and input tensors into tiles (blocks) for efficient computation. 
To fully utilize GPU resources, this tile structure should be considered, which is the goal of block pruning. 
However, previous block pruning prunes both row vectors and column vectors. 
Pruning of row vectors in a tile corresponds to filter pruning, and it also interferes with column-wise block pruning of the following layer.
In contrast, column vectors are much smaller than row vectors and can achieve lower accuracy drop.
Additionally, if the pruning ratio for each tile is different,
GPU utilization can be limited by imbalanced workloads by irregular-sized blocks.
The same pruning ratio for the weight tiles processed in parallel enables the actual inference process to fully utilize the resources without idle time.
This paper proposes balanced column-wise block pruning, named BCBP, to satisfy two conditions: the column-wise minimal size of the pruning unit and balanced workloads. 
We demonstrate that BCBP is superior to previous pruning methods through comprehensive experiments.

Balanced Column-Wise Block Pruning for Maximizing GPU Parallelism

Weakly supervised detection of anomalies in surveillance videos is a challenging task. Going beyond existing works that have deficient capabilities to localize anomalies in long videos, we propose a novel glance and focus network to effectively integrate spatial-temporal information for accurate anomaly detection. In addition, we empirically found that existing approaches that use feature magnitudes to represent the degree of anomalies typically ignore the effects of scene variations, and hence result in sub-optimal performance due to the inconsistency of feature magnitudes across scenes. To address this issue, we propose the Feature Amplification Mechanism and a Magnitude Contrastive Loss to enhance the discriminativeness of feature magnitudes for detecting anomalies. Experimental results on two large-scale benchmarks UCF-Crime and XD-Violence manifest that our method outperforms state-of-the-art approaches.

MGFN : Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly Detection

The creation of a parameterized stylized character involves careful selection of numerous parameters, also known as the "avatar vectors" that can be interpreted by the avatar engine. Existing unsupervised avatar vector estimation methods that auto-create avatars for users, however, often fail to work because of the domain gap between realistic faces and stylized avatar images. To this end, we propose SwiftAvatar, a novel avatar auto-creation framework that is evidently superior to previous works. SwiftAvatar introduces dual-domain generators to create pairs of realistic faces and avatar images using shared latent codes. The latent codes can then be bridged with the avatar vectors as pairs, by performing GAN inversion on the avatar images rendered from the engine using avatar vectors. Through this way, we are able to synthesize paired data in high-quality as many as possible, consisting of avatar vectors and their corresponding realistic faces. We also propose semantic augmentation to improve the diversity of synthesis. Finally, a light-weight avatar vector estimator is trained on the synthetic pairs to implement efficient auto-creation. Our experiments demonstrate the effectiveness and efficiency of SwiftAvatar on two different avatar engines. The superiority and advantageous flexibility of SwiftAvatar are also verified in both subjective and objective evaluations.

SwiftAvatar: Efficient Auto-Creation of Parameterized Stylized Character on Arbitrary Avatar Engines

Molecular dynamics (MD) has long been the de facto choice for simulating complex atomistic systems from first principles. Recently deep learning models become a popular way to accelerate MD. Notwithstanding, existing models depend on intermediate variables such as the potential energy or force fields to update atomic positions, which requires additional computations to perform back-propagation. To waive this requirement, we propose a novel model called DiffMD by directly estimating the gradient of the log density of molecular conformations. DiffMD relies on a score-based denoising diffusion generative model that perturbs the molecular structure with a conditional noise depending on atomic accelerations and treats conformations at previous timeframes as the prior distribution for sampling}. Another challenge of modeling such a conformation generation process is that a molecule is kinetic instead of static, which no prior works have strictly studied. To solve this challenge, we propose an equivariant geometric Transformer as the score function in the diffusion process to calculate corresponding gradients. It incorporates the directions and velocities of atomic motions via 3D spherical Fourier-Bessel representations. With multiple architectural improvements, we outperform state-of-the-art baselines on MD17 and isomers of C7O2H10 datasets. This work contributes to accelerating material and drug discovery.

DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations

Despite the excellent performance, deep neural networks (DNNs) have been shown to be vulnerable to adversarial examples. Besides, these examples are often transferable among different models. In other words, the same adversarial example can fool multiple models with different architectures at the same time. Based on this property, many black-box transfer-based attack techniques have been developed. However, current transfer-based attacks generally focus on the cross-architecture setting, where the attacker has access to the training data of the target model, which is not guaranteed in realistic situations. In this paper, we design a Cross-Domain Transfer-Based Attack (CDTA), which works in the cross-domain scenario. In this setting, attackers have no information about the target model, such as its architecture and training data. Specifically, we propose a contrastive spectral training method to train a feature extractor on a source domain (e.g., ImageNet) and use it to craft adversarial examples on target domains (e.g., Oxford 102 Flower). Our method corrupts the semantic information of the benign image by scrambling the outputs of both the intermediate feature layers and the final layer of the feature extractor. We evaluate CDTA with 16 target deep models on four datasets with widely varying styles. The results confirm that, in terms of the attack success rate, our approach can consistently outperform the state-of-the-art baselines by an average of 11.45% across all target models. Our code is available at https://github.com/LiulietLee/CDTA.

CDTA: A Cross-Domain Transfer-Based Attack with Contrastive Learning

Model compression and model defense for deep neural networks (DNNs) have been extensively and individually studied. Considering the co-importance of model compactness and robustness in practical applications, several prior works have explored to improve the adversarial robustness of the sparse neural networks. However, the structured sparse models obtained by the exiting works suffer severe performance degradation for both benign and robust accuracy, thereby causing a challenging dilemma between robustness and structuredness of the compact DNNs.
To address this problem, in this paper, we propose CSTAR, an efficient solution that can simultaneously impose the low-rankness-based Compactness, high STructuredness and high Adversarial Robustness on the target DNN models. By formulating the low-rankness and robustness requirement within the same framework and globally determining the ranks, the compressed DNNs can simultaneously achieve high compression performance and strong adversarial robustness. Evaluations for various DNN models on different datasets demonstrate the effectiveness of CSTAR. Compared with the state-of-the-art robust structured pruning methods, CSTAR shows consistently better performance. For instance, when compressing ResNet-18 on CIFAR-10, CSTAR can achieve up to 20.07% and 11.91% improvement for benign accuracy and robust accuracy, respectively. For compressing ResNet-18 with 16x compression ratio on Imagenet, CSTAR can obtain 8.58% benign accuracy gain and 4.27% robust accuracy gain compared to the existing robust structured pruning method.

CSTAR: Towards Compact and Structured Deep Neural Networks with Adversarial Robustness

One major limitation of CNNs is that they are vulnerable to adversarial attacks. Currently, adversarial robustness in neural networks is commonly optimized with respect to a small pre-selected adversarial noise strength, causing them to have potentially limited performance when under attack by larger adversarial noises in real-world scenarios. In this research, we aim to find Neural Architectures that have improved robustness on a wide range of adversarial noise strengths through Neural Architecture Search. In detail, we propose a lightweight Adversarial Noise Estimator to reduce the high cost of generating adversarial noise with respect to different strengths. Besides, we construct an Efficient Wide Spectrum Searcher to reduce the cost of adjusting network architecture with the large adversarial validation set during the search. With the two components proposed, the number of adversarial noise strengths searched can be increased significantly while having a limited increase in search time. Extensive experiments on benchmark datasets such as CIFAR and ImageNet demonstrate that with a significantly richer search signal in robustness, our method can find architectures with improved overall robustness while having a limited impact on natural accuracy and around 40% reduction in search time compared with the naive approach of searching. Codes available at: https://github.com/zhicheng2T0/Wsr-NAS.git

Neural Architecture Search for Wide Spectrum Adversarial Robustness

Any classifier can be "smoothed out" under Gaussian noise to build a new classifier that is provably robust to l2-adversarial perturbations, viz., by averaging its predictions over the noise via randomized smoothing. Under the smoothed classifiers, the fundamental trade-off between accuracy and (adversarial) robustness has been well evidenced in the literature: i.e., increasing the robustness of a classifier for an input can be at the expense of decreased accuracy for some other inputs. In this paper, we propose a simple training method leveraging this trade-off to obtain robust smoothed classifiers, in particular, through a sample-wise control of robustness over the training samples. We make this control feasible by using "accuracy under Gaussian noise" as an easy-to-compute proxy of adversarial robustness for an input. Specifically, we differentiate the training objective depending on this proxy to filter out samples that are unlikely to benefit from the worst-case (adversarial) objective. Our experiments show that the proposed method, despite its simplicity, consistently exhibits improved certified robustness upon state-of-the-art training methods. Somewhat surprisingly, we find these improvements persist even for other notions of robustness, e.g., to various types of common corruptions. Code is available at https://github.com/alinlab/smoothing-catrs.

Confidence-aware Training of Smoothed Classifiers for Certified Robustness

The Datalog query language can express several powerful recursive properties, often crucial in real-world scenarios. While answering such queries is feasible over relational databases, the picture changes dramatically when data is enriched with intensional knowledge. It is indeed well-known that answering Datalog queries is undecidable already over lightweight knowledge bases (KBs) of the DL-Lite family. To overcome this issue, we propose a new query language based on Disjunctive Datalog rules combined with a modal epistemic operator. Rules in this language interact with the queried KB exclusively via the epistemic operator, thus extracting only the information true in every model of the KB. This form of interaction is crucial for not falling into undecidability. The contribution provided by this paper is threefold. First, we illustrate the syntax and the semantics of the novel query language. Second, we study the expressive power of different fragments of our new language and compare it with Disjunctive Datalog and its variants. Third, we outline the precise data complexity of answering queries in our new language over KBs expressed in various well-known formalisms.

Epistemic Disjunctive Datalog for Querying Knowledge Bases

Many real-world sequential decision problems can be decomposed into processes with independent dynamics that are coupled via the action structure. I discuss recent work on such problems and future directions.

Exploring Tuning Characteristics of Ventral Stream's Neurons for Few-Shot Image Classification

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