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Big Transfer (BiT): General Visual Representation Learning

Big Transfer (BiT): General Visual Representation Learning

24 December 2019
Alexander Kolesnikov
Lucas Beyer
Xiaohua Zhai
J. Puigcerver
Jessica Yung
Sylvain Gelly
N. Houlsby
    MQ
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Papers citing "Big Transfer (BiT): General Visual Representation Learning"

50 / 724 papers shown
Title
Are we ready for a new paradigm shift? A Survey on Visual Deep MLP
Are we ready for a new paradigm shift? A Survey on Visual Deep MLP
Ruiyang Liu
Yinghui Li
Li Tao
Dun Liang
Haitao Zheng
82
96
0
07 Nov 2021
AUTOKD: Automatic Knowledge Distillation Into A Student Architecture
  Family
AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family
Roy Henha Eyono
Fabio Maria Carlucci
P. Esperança
Binxin Ru
Phillip Torr
19
3
0
05 Nov 2021
LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs
Christoph Schuhmann
Richard Vencu
Romain Beaumont
R. Kaczmarczyk
Clayton Mullis
Aarush Katta
Theo Coombes
J. Jitsev
Aran Komatsuzaki
VLM
MLLM
CLIP
10
1,371
0
03 Nov 2021
Meta-Learning to Improve Pre-Training
Meta-Learning to Improve Pre-Training
Aniruddh Raghu
Jonathan Lorraine
Simon Kornblith
Matthew B. A. McDermott
D. Duvenaud
19
30
0
02 Nov 2021
Evaluating deep transfer learning for whole-brain cognitive decoding
Evaluating deep transfer learning for whole-brain cognitive decoding
A. Thomas
U. Lindenberger
Wojciech Samek
K. Müller
AI4CE
11
12
0
01 Nov 2021
Projected GANs Converge Faster
Projected GANs Converge Faster
Axel Sauer
Kashyap Chitta
Jens Muller
Andreas Geiger
36
233
0
01 Nov 2021
CvS: Classification via Segmentation For Small Datasets
CvS: Classification via Segmentation For Small Datasets
Nooshin Mojab
Philip S. Yu
J. Hallak
Darvin Yi
15
4
0
29 Oct 2021
Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data
Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data
Gongfan Fang
Yifan Bao
Jie Song
Xinchao Wang
Don Xie
Chengchao Shen
Mingli Song
29
44
0
27 Oct 2021
Towards artificial general intelligence via a multimodal foundation
  model
Towards artificial general intelligence via a multimodal foundation model
Nanyi Fei
Zhiwu Lu
Yizhao Gao
Guoxing Yang
Yuqi Huo
...
Ruihua Song
Xin Gao
Tao Xiang
Haoran Sun
Jiling Wen
AI4CE
LRM
6
213
0
27 Oct 2021
Beyond Classification: Knowledge Distillation using Multi-Object
  Impressions
Beyond Classification: Knowledge Distillation using Multi-Object Impressions
Gaurav Kumar Nayak
Monish Keswani
Sharan Seshadri
Anirban Chakraborty
8
2
0
27 Oct 2021
The Efficiency Misnomer
The Efficiency Misnomer
Daoyuan Chen
Liuyi Yao
Dawei Gao
Ashish Vaswani
Yaliang Li
32
98
0
25 Oct 2021
Domain Adaptation and Active Learning for Fine-Grained Recognition in
  the Field of Biodiversity
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity
Bernd Gruner
Matthias Körschens
Björn Barz
Joachim Denzler
14
0
0
22 Oct 2021
Wav2CLIP: Learning Robust Audio Representations From CLIP
Wav2CLIP: Learning Robust Audio Representations From CLIP
Ho-Hsiang Wu
Prem Seetharaman
Kundan Kumar
J. P. Bello
CLIP
VLM
31
267
0
21 Oct 2021
No One Representation to Rule Them All: Overlapping Features of Training
  Methods
No One Representation to Rule Them All: Overlapping Features of Training Methods
Raphael Gontijo-Lopes
Yann N. Dauphin
E. D. Cubuk
18
60
0
20 Oct 2021
When in Doubt, Summon the Titans: Efficient Inference with Large Models
When in Doubt, Summon the Titans: Efficient Inference with Large Models
A. S. Rawat
Manzil Zaheer
A. Menon
Amr Ahmed
Sanjiv Kumar
17
7
0
19 Oct 2021
Unsupervised Finetuning
Unsupervised Finetuning
Suichan Li
Dongdong Chen
Yinpeng Chen
Lu Yuan
Lei Zhang
Qi Chu
B. Liu
Nenghai Yu
25
8
0
18 Oct 2021
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot
  Learning
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning
Yang Shu
Zhangjie Cao
Jing Gao
Jianmin Wang
Philip S. Yu
Mingsheng Long
30
10
0
14 Oct 2021
On the Security Risks of AutoML
On the Security Risks of AutoML
Ren Pang
Zhaohan Xi
S. Ji
Xiapu Luo
Ting Wang
AAML
11
10
0
12 Oct 2021
Rethinking Supervised Pre-training for Better Downstream Transferring
Rethinking Supervised Pre-training for Better Downstream Transferring
Yutong Feng
Jianwen Jiang
Mingqian Tang
R. L. Jin
Yue Gao
SSL
43
39
0
12 Oct 2021
Parameterizing Activation Functions for Adversarial Robustness
Parameterizing Activation Functions for Adversarial Robustness
Sihui Dai
Saeed Mahloujifar
Prateek Mittal
AAML
42
32
0
11 Oct 2021
RankingMatch: Delving into Semi-Supervised Learning with Consistency
  Regularization and Ranking Loss
RankingMatch: Delving into Semi-Supervised Learning with Consistency Regularization and Ranking Loss
Trung Q. Tran
Mingu Kang
Daeyoung Kim
11
2
0
09 Oct 2021
Adversarial Token Attacks on Vision Transformers
Adversarial Token Attacks on Vision Transformers
Ameya Joshi
Gauri Jagatap
C. Hegde
ViT
30
19
0
08 Oct 2021
Sparse MoEs meet Efficient Ensembles
Sparse MoEs meet Efficient Ensembles
J. Allingham
F. Wenzel
Zelda E. Mariet
Basil Mustafa
J. Puigcerver
...
Balaji Lakshminarayanan
Jasper Snoek
Dustin Tran
Carlos Riquelme Ruiz
Rodolphe Jenatton
MoE
44
21
0
07 Oct 2021
Exploring the Limits of Large Scale Pre-training
Exploring the Limits of Large Scale Pre-training
Samira Abnar
Mostafa Dehghani
Behnam Neyshabur
Hanie Sedghi
AI4CE
55
114
0
05 Oct 2021
On the Importance of Gradients for Detecting Distributional Shifts in
  the Wild
On the Importance of Gradients for Detecting Distributional Shifts in the Wild
Rui Huang
Andrew Geng
Yixuan Li
180
328
0
01 Oct 2021
Robust Temporal Ensembling for Learning with Noisy Labels
Robust Temporal Ensembling for Learning with Noisy Labels
Abel Brown
Benedikt D. Schifferer
R. DiPietro
NoLa
OOD
6
0
0
29 Sep 2021
Back in Black: A Comparative Evaluation of Recent State-Of-The-Art
  Black-Box Attacks
Back in Black: A Comparative Evaluation of Recent State-Of-The-Art Black-Box Attacks
Kaleel Mahmood
Rigel Mahmood
Ethan Rathbun
Marten van Dijk
AAML
14
22
0
29 Sep 2021
Balanced-MixUp for Highly Imbalanced Medical Image Classification
Balanced-MixUp for Highly Imbalanced Medical Image Classification
Adrian Galdran
G. Carneiro
M. A. G. Ballester
18
99
0
20 Sep 2021
A Study of the Generalizability of Self-Supervised Representations
A Study of the Generalizability of Self-Supervised Representations
Atharva Tendle
Mohammad Rashedul Hasan
68
26
0
19 Sep 2021
EfficientCLIP: Efficient Cross-Modal Pre-training by Ensemble Confident
  Learning and Language Modeling
EfficientCLIP: Efficient Cross-Modal Pre-training by Ensemble Confident Learning and Language Modeling
Jue Wang
Haofan Wang
Jincan Deng
Weijia Wu
Debing Zhang
VLM
CLIP
61
18
0
10 Sep 2021
Robust fine-tuning of zero-shot models
Robust fine-tuning of zero-shot models
Mitchell Wortsman
Gabriel Ilharco
Jong Wook Kim
Mike Li
Simon Kornblith
...
Raphael Gontijo-Lopes
Hannaneh Hajishirzi
Ali Farhadi
Hongseok Namkoong
Ludwig Schmidt
VLM
23
688
0
04 Sep 2021
Revisiting 3D ResNets for Video Recognition
Revisiting 3D ResNets for Video Recognition
Xianzhi Du
Yeqing Li
Yin Cui
Rui Qian
Jing Li
Irwan Bello
51
17
0
03 Sep 2021
AP-10K: A Benchmark for Animal Pose Estimation in the Wild
AP-10K: A Benchmark for Animal Pose Estimation in the Wild
Hang Yu
Yufei Xu
Jing Zhang
Wei Zhao
Ziyu Guan
Dacheng Tao
13
107
0
28 Aug 2021
Towards Fine-grained Image Classification with Generative Adversarial
  Networks and Facial Landmark Detection
Towards Fine-grained Image Classification with Generative Adversarial Networks and Facial Landmark Detection
Mahdieh Darvish
Mahsa Pouramini
H. Bahador
ViT
17
5
0
28 Aug 2021
Multi-Task Self-Training for Learning General Representations
Multi-Task Self-Training for Learning General Representations
Golnaz Ghiasi
Barret Zoph
E. D. Cubuk
Quoc V. Le
Tsung-Yi Lin
SSL
13
100
0
25 Aug 2021
A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your
  Pre-training Effective?
A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?
Hiroaki Mikami
Kenji Fukumizu
Shogo Murai
Shuji Suzuki
Yuta Kikuchi
Taiji Suzuki
S. Maeda
Kohei Hayashi
38
12
0
25 Aug 2021
PatchCleanser: Certifiably Robust Defense against Adversarial Patches
  for Any Image Classifier
PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier
Chong Xiang
Saeed Mahloujifar
Prateek Mittal
VLM
AAML
11
73
0
20 Aug 2021
Do Vision Transformers See Like Convolutional Neural Networks?
Do Vision Transformers See Like Convolutional Neural Networks?
M. Raghu
Thomas Unterthiner
Simon Kornblith
Chiyuan Zhang
Alexey Dosovitskiy
ViT
41
922
0
19 Aug 2021
Challenges for cognitive decoding using deep learning methods
Challenges for cognitive decoding using deep learning methods
A. Thomas
Christopher Ré
R. Poldrack
AI4CE
16
6
0
16 Aug 2021
Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual
  Representations
Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations
Josh Beal
Hao Wu
Dong Huk Park
Andrew Zhai
Dmitry Kislyuk
ViT
13
29
0
12 Aug 2021
ConvNets vs. Transformers: Whose Visual Representations are More
  Transferable?
ConvNets vs. Transformers: Whose Visual Representations are More Transferable?
Hong-Yu Zhou
Chi-Ken Lu
Sibei Yang
Yizhou Yu
ViT
24
54
0
11 Aug 2021
SoK: How Robust is Image Classification Deep Neural Network
  Watermarking? (Extended Version)
SoK: How Robust is Image Classification Deep Neural Network Watermarking? (Extended Version)
Nils Lukas
Edward Jiang
Xinda Li
Florian Kerschbaum
AAML
28
86
0
11 Aug 2021
On the Effect of Pruning on Adversarial Robustness
On the Effect of Pruning on Adversarial Robustness
Artur Jordão
Hélio Pedrini
AAML
32
22
0
10 Aug 2021
Elaborative Rehearsal for Zero-shot Action Recognition
Elaborative Rehearsal for Zero-shot Action Recognition
Shizhe Chen
Dong Huang
VLM
22
93
0
05 Aug 2021
Semi-weakly Supervised Contrastive Representation Learning for Retinal
  Fundus Images
Semi-weakly Supervised Contrastive Representation Learning for Retinal Fundus Images
Boon Peng Yap
B. Ng
SSL
17
3
0
04 Aug 2021
Improve Unsupervised Pretraining for Few-label Transfer
Improve Unsupervised Pretraining for Few-label Transfer
Suichan Li
Dongdong Chen
Yinpeng Chen
Lu Yuan
Lei Zhang
Qi Chu
B. Liu
Nenghai Yu
SSL
10
20
0
26 Jul 2021
Characterizing Generalization under Out-Of-Distribution Shifts in Deep
  Metric Learning
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning
Timo Milbich
Karsten Roth
Samarth Sinha
Ludwig Schmidt
Marzyeh Ghassemi
Bjorn Ommer
OOD
OODD
23
22
0
20 Jul 2021
Non-binary deep transfer learning for image classification
Non-binary deep transfer learning for image classification
J. Plested
Xuyang Shen
Tom Gedeon
MQ
30
5
0
19 Jul 2021
Visual Representation Learning Does Not Generalize Strongly Within the
  Same Domain
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
Lukas Schott
Julius von Kügelgen
Frederik Trauble
Peter V. Gehler
Chris Russell
Matthias Bethge
Bernhard Schölkopf
Francesco Locatello
Wieland Brendel
OOD
DRL
35
66
0
17 Jul 2021
The Benchmark Lottery
The Benchmark Lottery
Mostafa Dehghani
Yi Tay
A. Gritsenko
Zhe Zhao
N. Houlsby
Fernando Diaz
Donald Metzler
Oriol Vinyals
34
89
0
14 Jul 2021
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