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Found 10 papers, 2 papers with code
Rethinking Class Activation Mapping for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) is a task of localizing an object in an image only using image-level labels.
AdaFlood: Adaptive Flood Regularization
Although neural networks are conventionally optimized towards zero training loss, it has been recently learned that targeting a non-zero training loss threshold, referred to as a flood level, often enables better test time generalization.
Exploring Active Learning in Meta-Learning: Enhancing Context Set Labeling
Most meta-learning methods assume that the (very small) context set used to establish a new task at test time is passively provided.
How to prepare your task head for finetuning
We identify a significant trend in the effect of changes in this initial energy on the resulting features after fine-tuning.
Meta Temporal Point Processes
In this work, we propose to train TPPs in a meta learning framework, where each sequence is treated as a different task, via a novel framing of TPPs as neural processes (NPs).
Object Discovery via Contrastive Learning for Weakly Supervised Object Detection
Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations.
Ranked #1 on
Weakly Supervised Object Detection
on MS-COCO-2017
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels
We propose a new method for approximating active learning acquisition strategies that are based on retraining with hypothetically-labeled candidate data points.
A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel
Empirical neural tangent kernels (eNTKs) can provide a good understanding of a given network's representation: they are often far less expensive to compute and applicable more broadly than infinite width NTKs.
One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model
Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model to identify objects in images based on a small number of images with pixel-level labels, and many more images with only image-level labels.
LID 2020: The Learning from Imperfect Data Challenge Results
The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in developing novel approaches that would harness the imperfect data and improve the data-efficiency during training.
