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Found 21 papers, 11 papers with code
PointCFormer: a Relation-based Progressive Feature Extraction Network for Point Cloud Completion
Meanwhile, traditional local feature extraction methods usually struggle to capture the intricate geometric details.
Articulatory Phonetics Informed Controllable Expressive Speech Synthesis
We open-source the dataset and TTS models.
Orchestrate Latent Expertise: Advancing Online Continual Learning with Multi-Level Supervision and Reverse Self-Distillation
However, a notable gap from CL to OCL stems from the additional overfitting-underfitting dilemma associated with the use of rehearsal buffers: the inadequate learning of new training samples (underfitting) and the repeated learning of a few old training samples (overfitting).
Attribute-Guided Pedestrian Retrieval: Bridging Person Re-ID with Internal Attribute Variability
In various domains such as surveillance and smart retail pedestrian retrieval centering on person re-identification (Re-ID) plays a pivotal role.
Multimodal Identification of Alzheimer's Disease: A Review
Alzheimer's disease is a progressive neurological disorder characterized by cognitive impairment and memory loss.
SynthTab: Leveraging Synthesized Data for Guitar Tablature Transcription
Guitar Tablature Transcription (GTT) is an important task with broad applications in music education, composition, and entertainment.
Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence
Continual learning aims to empower artificial intelligence (AI) with strong adaptability to the real world.
Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images
Segmentation of the infected areas of the lung is essential for quantifying the severity of lung disease like pulmonary infections.
EE-TTS: Emphatic Expressive TTS with Linguistic Information
EE-TTS contains an emphasis predictor that can identify appropriate emphasis positions from text and a conditioned acoustic model to synthesize expressive speech with emphasis and linguistic information.
Hebbian and Gradient-based Plasticity Enables Robust Memory and Rapid Learning in RNNs
Comparing different plasticity rules under the same framework shows that Hebbian plasticity is well-suited for several memory and associative learning tasks; however, it is outperformed by gradient-based plasticity on few-shot regression tasks which require the model to infer the underlying mapping.
CoSCL: Cooperation of Small Continual Learners is Stronger than a Big One
Continual learning requires incremental compatibility with a sequence of tasks.
Interference-Limited Ultra-Reliable and Low-Latency Communications: Graph Neural Networks or Stochastic Geometry?
In this paper, we aim to improve the Quality-of-Service (QoS) of Ultra-Reliability and Low-Latency Communications (URLLC) in interference-limited wireless networks.
Memory Replay with Data Compression for Continual Learning
In this work, we propose memory replay with data compression (MRDC) to reduce the storage cost of old training samples and thus increase their amount that can be stored in the memory buffer.
AFEC: Active Forgetting of Negative Transfer in Continual Learning
Without accessing to the old training samples, knowledge transfer from the old tasks to each new task is difficult to determine, which might be either positive or negative.
Few-shot Continual Learning: a Brain-inspired Approach
Our solution is based on the observation that continual learning of a task sequence inevitably interferes few-shot generalization, which makes it highly nontrivial to extend few-shot learning strategies to continual learning scenarios.
Clothing Status Awareness for Long-Term Person Re-Identification
This work argues that these approaches in fact are not aware of clothing status (i. e., change or no-change) of a pedestrian.
Using Deep Convolutional Neural Networks to Diagnose COVID-19 From Chest X-Ray Images
The COVID-19 epidemic has become a major safety and health threat worldwide.
Triple Memory Networks: a Brain-Inspired Method for Continual Learning
Continual acquisition of novel experience without interfering previously learned knowledge, i. e. continual learning, is critical for artificial neural networks, but limited by catastrophic forgetting.
Graph-augmented Convolutional Networks on Drug-Drug Interactions Prediction
We propose an end-to-end model to predict drug-drug interactions (DDIs) by employing graph-augmented convolutional networks.
Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification
A representative sequential convolutional recurrent neural network architecture with the two-layer convolutional neural network and subsequent two-layer long short-term memory neural network is developed to suggest the option for fast automatic modulation classification.
SBSGAN: Suppression of Inter-Domain Background Shift for Person Re-Identification
We observe that if backgrounds in the training and testing datasets are very different, it dramatically introduces difficulties to extract robust pedestrian features, and thus compromises the cross-domain person re-ID performance.
