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Found 21 papers, 14 papers with code
AmadeusGPT: a natural language interface for interactive animal behavioral analysis
To overcome the context window limitation, we implement a novel dual-memory mechanism to allow communication between short-term and long-term memory using symbols as context pointers for retrieval and saving.
Enhance the Visual Representation via Discrete Adversarial Training
For borrowing the advantage from NLP-style AT, we propose Discrete Adversarial Training (DAT).
Ranked #1 on
Domain Generalization
on Stylized-ImageNet
Multi-animal pose estimation, identification and tracking with DeepLabCut
Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios.
Ranked #4 on
Animal Pose Estimation
on TriMouse-161
SuperAnimal pretrained pose estimation models for behavioral analysis
We illustrate the utility of our models in behavioral classification in mice and gait analysis in horses.
Ranked #1 on
Animal Pose Estimation
on Animal-Pose Dataset
(using extra training data)
Towards Robust Vision Transformer
By using and combining robust components as building blocks of ViTs, we propose Robust Vision Transformer (RVT), which is a new vision transformer and has superior performance with strong robustness.
Ranked #24 on
Domain Generalization
on ImageNet-C
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink
Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted from some physical source as adversarial attacker on DNNs in a real-world scenario.
QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval
Comprehensive experiments show that the proposed attack achieves a high attack success rate with few queries against the image retrieval systems under the black-box setting.
PCNN: Pattern-based Fine-Grained Regular Pruning towards Optimizing CNN Accelerators
Weight pruning is a powerful technique to realize model compression.
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
Existing domain adaptation methods aim at learning features that can be generalized among domains.
Ranked #3 on
Domain Adaptation
on USPS-to-MNIST
Adversarial Robustness vs. Model Compression, or Both?
Furthermore, this work studies two hypotheses about weight pruning in the conventional setting and finds that weight pruning is essential for reducing the network model size in the adversarial setting; training a small model from scratch even with inherited initialization from the large model cannot achieve neither adversarial robustness nor high standard accuracy.
Non-Structured DNN Weight Pruning -- Is It Beneficial in Any Platform?
Based on the proposed comparison framework, with the same accuracy and quantization, the results show that non-structrued pruning is not competitive in terms of both storage and computation efficiency.
Brain-inspired reverse adversarial examples
On contrast, current state-of-the-art deep learning approaches heavily depend on the variety of training samples and the capacity of the network.
Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM
Weight quantization is one of the most important techniques of Deep Neural Networks (DNNs) model compression method.
Adversarial Robustness vs Model Compression, or Both?
Furthermore, this work studies two hypotheses about weight pruning in the conventional setting and finds that weight pruning is essential for reducing the network model size in the adversarial setting, training a small model from scratch even with inherited initialization from the large model cannot achieve both adversarial robustness and high standard accuracy.
Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM
A recent work developed a systematic frame-work of DNN weight pruning using the advanced optimization technique ADMM (Alternating Direction Methods of Multipliers), achieving one of state-of-art in weight pruning results.
ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers
The first part of ADMM-NN is a systematic, joint framework of DNN weight pruning and quantization using ADMM.
A Unified Framework of DNN Weight Pruning and Weight Clustering/Quantization Using ADMM
Both DNN weight pruning and clustering/quantization, as well as their combinations, can be solved in a unified manner.
Progressive Weight Pruning of Deep Neural Networks using ADMM
Motivated by dynamic programming, the proposed method reaches extremely high pruning rate by using partial prunings with moderate pruning rates.
StructADMM: A Systematic, High-Efficiency Framework of Structured Weight Pruning for DNNs
Without loss of accuracy on the AlexNet model, we achieve 2. 58X and 3. 65X average measured speedup on two GPUs, clearly outperforming the prior work.
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
We first formulate the weight pruning problem of DNNs as a nonconvex optimization problem with combinatorial constraints specifying the sparsity requirements, and then adopt the ADMM framework for systematic weight pruning.
Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers
We present a systematic weight pruning framework of deep neural networks (DNNs) using the alternating direction method of multipliers (ADMM).
