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04/07/2023
Rethinking Evaluation Protocols of Visual Representations Learned via Self-supervised Learning
Linear probing (LP) (and k-NN) on the upstream dataset with labels (e.g....
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04/03/2023
Few-shot Fine-tuning is All You Need for Source-free Domain Adaptation
Recently, source-free unsupervised domain adaptation (SFUDA) has emerged...
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03/25/2023
Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions
Active learning aims to identify the most informative data from an unlab...
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12/01/2021
A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks
Deep neural networks have achieved outstanding performance over various ...
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11/02/2021
Elucidating Noisy Data via Uncertainty-Aware Robust Learning
Robust learning methods aim to learn a clean target distribution from no...
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07/03/2020
Confidence-Aware Learning for Deep Neural Networks
Despite the power of deep neural networks for a wide range of tasks, an ...
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06/22/2020
Self-Knowledge Distillation: A Simple Way for Better Generalization
The generalization capability of deep neural networks has been substanti...
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07/22/2018
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
Tumor proliferation is an important biomarker indicative of the prognosi...
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08/02/2017
Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks
We introduce an accurate lung segmentation model for chest radiographs b...
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12/21/2016
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
We present a unified framework to predict tumor proliferation scores fro...
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11/04/2016
Semantic Noise Modeling for Better Representation Learning
Latent representation learned from multi-layered neural networks via hie...
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02/16/2016
Deconvolutional Feature Stacking for Weakly-Supervised Semantic Segmentation
A weakly-supervised semantic segmentation framework with a tied deconvol...
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02/04/2016