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Found 13 papers, 10 papers with code
How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?
This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification.
Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient Representations
This motivates us to shift the paradigm from combining loss at the end, to choosing the proper learning method per network layer.
Convergence and Generalization of Wide Neural Networks with Large Bias
This work studies training one-hidden-layer overparameterized ReLU networks via gradient descent in the neural tangent kernel (NTK) regime, where the networks' biases are initialized to some constant rather than zero.
M$^3$ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design
However, when deploying MTL onto those real-world systems that are often resource-constrained or latency-sensitive, two prominent challenges arise: (i) during training, simultaneously optimizing all tasks is often difficult due to gradient conflicts across tasks; (ii) at inference, current MTL regimes have to activate nearly the entire model even to just execute a single task.
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study
Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings.
Ranked #1 on
Long-tail Learning
on MIMIC-CXR-LT
Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling
Contrastive learning approaches have achieved great success in learning visual representations with few labels of the target classes.
Self-Damaging Contrastive Learning
Hence, the key innovation in SDCLR is to create a dynamic self-competitor model to contrast with the target model, which is a pruned version of the latter.
Improving Contrastive Learning on Imbalanced Data via Open-World Sampling
Contrastive learning approaches have achieved great success in learning visual representations with few labels of the target classes.
Robust Pre-Training by Adversarial Contrastive Learning
Recent work has shown that, when integrated with adversarial training, self-supervised pre-training can lead to state-of-the-art robustness In this work, we improve robustness-aware self-supervised pre-training by learning representations that are consistent under both data augmentations and adversarial perturbations.
Peek-a-Boo: Occlusion Reasoning in Indoor Scenes With Plane Representations
It consists of a two-branch category-specific module that aims to predict layout and objects of the scene separately so that different types of planes can be handled better.
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings
Extensive simulations and ablation studies, with real energy measurements from an FPGA board, confirm the superiority of our proposed strategies and demonstrate remarkable energy savings for training.
ArcticNet: A Deep Learning Solution to Classify Arctic Wetlands
Arctic environments are rapidly changing under the warming climate.
Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images
In either way, the loss of local fine details or global contextual information results in limited segmentation accuracy.
Ranked #4 on
Land Cover Classification
on DeepGlobe
