MAGIc@Bath Research Group
MAGIc@Bath Research Group
Tour
News
People
Projects
Publications
Contact
Pravendra Singh
Latest
Rectification-Based Knowledge Retention for Task Incremental Learning
Context extraction module for deep convolutional neural networks
Fair Visual Recognition in Limited Data Regime using Self-Supervision and Self-Distillation
AVGZSLNet: Audio-Visual Generalized Zero-Shot Learning by Reconstructing Label Features from Multi-Modal Embeddings
Improving Few-Shot Learning using Composite Rotation based Auxiliary Task
Knowledge Consolidation based Class Incremental Online Learning with Limited Data
Rectification-Based Knowledge Retention for Continual Learning
RNNP: A Robust Few-Shot Learning Approach
A \"Network Pruning Network\" Approach to Deep Model Compression
Acceleration of Deep Convolutional Neural Networks Using Adaptive Filter Pruning
Accuracy Booster: Performance Boosting using Feature Map Re-calibration
Cooperative Initialization based Deep Neural Network Training
CPWC: Contextual Point Wise Convolution for Object Recognition
EDS pooling layer
FALF ConvNets: Fatuous auxiliary loss based filter-pruning for efficient deep CNNs
GIFSL - grafting based improved few-shot learning
Leveraging Filter Correlations for Deep Model Compression
Minimizing Supervision in Multi-label Categorization
Passive Batch Injection Training Technique: Boosting Network Performance by Injecting Mini-Batches from a different Data Distribution
SkipConv: Skip Convolution for Computationally Efficient Deep CNNs
HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs
Multi-Layer Pruning Framework for Compressing Single Shot MultiBox Detector
Play and Prune: Adaptive Filter Pruning for Deep Model Compression
Stability Based Filter Pruning for Accelerating Deep CNNs
Cite
×