Dancing with Images: Video Distillation via Static-Dynamic Disentanglement

Recently, dataset distillation has paved the way towards efficient machine learning, especially for image datasets. However, the distillation for videos, characterized by an exclusive temporal dimension, remains an underexplored domain. In this work, we provide the first systematic study of video distillation and introduce a taxonomy to categorize temporal compression. Our investigation reveals that the temporal information is usually not well learned during distillation , and the temporal dimension of synthetic data contributes little. The observations motivate our unified framework of disentangling the dynamic and static information in the videos. It first distills the videos into still images as static memory and then compensates the dynamic and motion information with a learnable dynamic memory block. Our method achieves state-of-the-art on video datasets at different scales, with notably smaller storage expenditure. Our code will be publicly available.

RigLSTM: Recurrent Independent Grid LSTM for Generalizable Sequence Learning

Sequential processes in real-world often carry a combination of simple subsystems that interact with each other in certain forms. Learning such a modular structure can often improve the robustness against environmental changes. In this paper, we propose recurrent independent Grid LSTM (RigLSTM), composed of a group of independent LSTM cells that cooperate with each other, for exploiting the underlying modular structure of the target task. Our model adopts cell selection, input feature selection, hidden state selection, and soft state updating to achieve a better generalization ability on the basis of the recent Grid LSTM for the tasks where some factors differ between training and evaluation. Specifically, at each time step, only a fraction of cells are activated, and the activated cells select relevant inputs and cells to communicate with. At the end of one time step, the hidden states of the activated cells are updated by considering the relevance between the inputs and the hidden states from the last and current time steps. Extensive experiments on diversified sequential modeling tasks are conducted to show the superior generalization ability when there exist changes in the testing environment. Source code is available at \url{https://github.com/ziyuwwang/rig-lstm}.

HUB: Guiding Learned Optimizers with Continuous Prompt Tuning

Learned optimizers are a crucial component of meta-learning. Recent advancements in scalable learned optimizers have demonstrated their superior performance over hand-designed optimizers in various tasks. However, certain characteristics of these models, such as an unstable learning curve, limited ability to handle unseen tasks and network architectures, difficult-to-control behaviours, and poor performance in fine-tuning tasks impede their widespread adoption. To tackle the issue of generalization in scalable learned optimizers, we propose a hybrid-update-based (HUB) optimization strategy inspired by recent advancements in hard prompt tuning and result selection techniques used in large language and vision models. This approach can be easily applied to any task that involves hand-designed or learned optimizer. By incorporating hand-designed optimizers as the second component in our hybrid approach, we are able to retain the benefits of learned optimizers while stabilizing the training process and, more importantly, improving testing performance. We validate our design through a total of 17 tasks, consisting of thirteen training from scratch and four fine-tuning settings. These tasks vary in model sizes, architectures, or dataset sizes, and the competing optimizers are hyperparameter-tuned. We outperform all competitors in 94% of the tasks with better testing performance. Furthermore, we conduct a theoretical analysis to examine the potential impact of our hybrid strategy on the behaviours and inherited traits of learned optimizers.

Distill Gold from Massive Ores: Efficient Dataset Distillation via Critical Samples Selection

Data-efficient learning has drawn significant attention, especially given the current trend of large multi-modal models, where dataset distillation can be an effective solution. However, the dataset distillation process itself is still very inefficient. In this work, we model the distillation problem with reference to information theory. Observing that severe data redundancy exists in dataset distillation, we argue to put more emphasis on the utility of the training samples. We propose a family of methods to exploit the most valuable samples, which is validated by our comprehensive analysis of the optimal data selection. The new strategy significantly reduces the training cost and extends a variety of existing distillation algorithms to larger and more diversified datasets, e.g. in some cases only 0.04% training data is sufficient for comparable distillation performance. Moreover, our strategy consistently enhances the performance, which may open up new analyses on the dynamics of distillation and networks. Our method is able to extend the distillation algorithms to much larger-scale datasets and more heterogeneous datasets, e.g. ImageNet-1K and Kinetics-400. Our code will be made publicly available.

Bulk-Switching Memristor-based Compute-In-Memory Module for Deep Neural Network Training

The need for deep neural network (DNN) models with higher performance and better functionality leads to the proliferation of very large models. Model training, however, requires intensive computation time and energy. Memristor-based compute-in-memory (CIM) modules can perform vector-matrix multiplication (VMM) in situ and in parallel, and have shown great promises in DNN inference applications. However, CIM-based model training faces challenges due to non-linear weight updates, device variations, and low-precision in analog computing circuits. In this work, we experimentally implement a mixed-precision training scheme to mitigate these effects using a bulk-switching memristor CIM module. Lowprecision CIM modules are used to accelerate the expensive VMM operations, with high precision weight updates accumulated in digital units. Memristor devices are only changed when the accumulated weight update value exceeds a pre-defined threshold. The proposed scheme is implemented with a system-on-chip (SoC) of fully integrated analog CIM modules and digital sub-systems, showing fast convergence of LeNet training to 97.73%. The efficacy of training larger models is evaluated using realistic hardware parameters and shows that that analog CIM modules can enable efficient mix-precision DNN training with accuracy comparable to full-precision software trained models. Additionally, models trained on chip are inherently robust to hardware variations, allowing direct mapping to CIM inference chips without additional re-training.

PowerGAN: A Machine Learning Approach for Power Side-Channel Attack on Compute-in-Memory Accelerators

Analog compute-in-memory (CIM) accelerators are becoming increasingly popular for deep neural network (DNN) inference due to their energy efficiency and in-situ vector-matrix multiplication (VMM) capabilities. However, as the use of DNNs expands, protecting user input privacy has become increasingly important. In this paper, we identify a security vulnerability wherein an adversary can reconstruct the user's private input data from a power side-channel attack, under proper data acquisition and pre-processing, even without knowledge of the DNN model. We further demonstrate a machine learning-based attack approach using a generative adversarial network (GAN) to enhance the reconstruction. Our results show that the attack methodology is effective in reconstructing user inputs from analog CIM accelerator power leakage, even when at large noise levels and countermeasures are applied. Specifically, we demonstrate the efficacy of our approach on the U-Net for brain tumor detection in magnetic resonance imaging (MRI) medical images, with a noise-level of 20% standard deviation of the maximum power signal value. Our study highlights a significant security vulnerability in analog CIM accelerators and proposes an effective attack methodology using a GAN to breach user privacy.

Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos

The success of the Neural Radiance Fields (NeRFs) for modeling and free-view rendering static objects has inspired numerous attempts on dynamic scenes. Current techniques that utilize neural rendering for facilitating free-view videos (FVVs) are restricted to either offline rendering or are capable of processing only brief sequences with minimal motion. In this paper, we present a novel technique, Residual Radiance Field or ReRF, as a highly compact neural representation to achieve real-time FVV rendering on long-duration dynamic scenes. ReRF explicitly models the residual information between adjacent timestamps in the spatial-temporal feature space, with a global coordinate-based tiny MLP as the feature decoder. Specifically, ReRF employs a compact motion grid along with a residual feature grid to exploit inter-frame feature similarities. We show such a strategy can handle large motions without sacrificing quality. We further present a sequential training scheme to maintain the smoothness and the sparsity of the motion/residual grids. Based on ReRF, we design a special FVV codec that achieves three orders of magnitudes compression rate and provides a companion ReRF player to support online streaming of long-duration FVVs of dynamic scenes. Extensive experiments demonstrate the effectiveness of ReRF for compactly representing dynamic radiance fields, enabling an unprecedented free-viewpoint viewing experience in speed and quality.

RN-Net: Reservoir Nodes-Enabled Neuromorphic Vision Sensing Network

Event-based cameras are inspired by the sparse and asynchronous spike representation of the biological visual system. However, processing the even data requires either using expensive feature descriptors to transform spikes into frames, or using spiking neural networks that are difficult to train. In this work, we propose a neural network architecture based on simple convolution layers integrated with dynamic temporal encoding reservoirs with low hardware and training costs. The Reservoir Nodes-enabled neuromorphic vision sensing Network (RN-Net) allows the network to efficiently process asynchronous temporal features, and achieves the highest accuracy of 99.2% for DVS128 Gesture reported to date, and one of the highest accuracy of 67.5% for DVS Lip dataset at a much smaller network size. By leveraging the internal dynamics of memristors, asynchronous temporal feature encoding can be implemented at very low hardware cost without preprocessing or dedicated memory and arithmetic units. The use of simple DNN blocks and backpropagation based training rules further reduces its implementation cost. Code will be publicly available.

NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks

Deep Learning (DL) and Deep Neural Networks (DNNs) are widely used in various domains. However, adversarial attacks can easily mislead a neural network and lead to wrong decisions. Defense mechanisms are highly preferred in safety-critical applications. In this paper, firstly, we use the gradient class activation map (GradCAM) to analyze the behavior deviation of the VGG-16 network when its inputs are mixed with adversarial perturbation or Gaussian noise. In particular, our method can locate vulnerable layers that are sensitive to adversarial perturbation and Gaussian noise. We also show that the behavior deviation of vulnerable layers can be used to detect adversarial examples. Secondly, we propose a novel NoiseCAM algorithm that integrates information from globally and pixel-level weighted class activation maps. Our algorithm is susceptible to adversarial perturbations and will not respond to Gaussian random noise mixed in the inputs. Third, we compare detecting adversarial examples using both behavior deviation and NoiseCAM, and we show that NoiseCAM outperforms behavior deviation modeling in its overall performance. Our work could provide a useful tool to defend against certain adversarial attacks on deep neural networks.