Search Results for author:
Found 9 papers, 8 papers with code
On Improving the Algorithm-, Model-, and Data- Efficiency of Self-Supervised Learning
In this paper, we propose an efficient single-branch SSL method based on non-parametric instance discrimination, aiming to improve the algorithm, model, and data efficiency of SSL.
Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning
We propose universally slimmable self-supervised learning (dubbed as US3L) to achieve better accuracy-efficiency trade-offs for deploying self-supervised models across different devices.
Synergistic Self-supervised and Quantization Learning
In this paper, we propose a method called synergistic self-supervised and quantization learning (SSQL) to pretrain quantization-friendly self-supervised models facilitating downstream deployment.
Worst Case Matters for Few-Shot Recognition
Few-shot recognition learns a recognition model with very few (e. g., 1 or 5) images per category, and current few-shot learning methods focus on improving the average accuracy over many episodes.
Training Vision Transformers with Only 2040 Images
Vision Transformers (ViTs) is emerging as an alternative to convolutional neural networks (CNNs) for visual recognition.
A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications
That is, a CNN has an inductive bias to naturally focus on objects, named as Tobias ("The object is at sight") in this paper.
Rethinking Self-Supervised Learning: Small is Beautiful
Self-supervised learning (SSL), in particular contrastive learning, has made great progress in recent years.
Rethinking the Route Towards Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) aims to localize objects with only image-level labels.
Ranked #2 on
Weakly-Supervised Object Localization
on CUB-200-2011
(Top-1 Localization Accuracy metric)
Neural Random Subspace
The random subspace method, known as the pillar of random forests, is good at making precise and robust predictions.
