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Found 10 papers, 1 papers with code
Continual Learning via Manifold Expansion Replay
To address this problem, we propose a novel replay strategy called Manifold Expansion Replay (MaER).
SmartPlay : A Benchmark for LLMs as Intelligent Agents
We introduce SmartPlay: both a challenging benchmark and a methodology for evaluating LLMs as agents.
Group Equivariant BEV for 3D Object Detection
Experimental results demonstrate that GeqBevNet can extract more rotational equivariant features in the 3D object detection of the actual road scene and improve the performance of object orientation prediction.
Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations
Motivated by the important role of ID, in this paper, we propose a novel deep representation learning approach with autoencoder, which incorporates regularization of the global and local ID constraints into the reconstruction of data representations.
Multi-scale Geometry-aware Transformer for 3D Point Cloud Classification
At first, the MGT divides point cloud data into patches with multiple scales.
Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification
We propose an algorithmic framework that leverages the advantages of the DNNs for data self-expression and task-specific predictions, to improve image classification.
ViR:the Vision Reservoir
To address these problems, a novel method, namely, Vision Reservoir computing (ViR), is proposed here for image classification, as a parallel to ViT.
Learning Robust and Lightweight Model through Separable Structured Transformations
Learning deep models with both lightweight and robustness is necessary for these equipments.
Joint Learning of Discriminative Low-dimensional Image Representations Based on Dictionary Learning and Two-layer Orthogonal Projections
There are some inadequacies in the language description of this paper that require further improvement.
1D-Convolutional Capsule Network for Hyperspectral Image Classification
The complex structure of CNNs results in prohibitive training efforts.
