Search Results for author: KaiFeng Chen

Found 8 papers, 5 papers with code

Learning Vision from Models Rivals Learning Vision from Data

1 code implementation28 Dec 2023 Yonglong Tian, Lijie Fan, KaiFeng Chen, Dina Katabi, Dilip Krishnan, Phillip Isola

We introduce SynCLR, a novel approach for learning visual representations exclusively from synthetic images and synthetic captions, without any real data.

Contrastive Learning Image Captioning +3

Scaling Laws of Synthetic Images for Model Training ... for Now

1 code implementation7 Dec 2023 Lijie Fan, KaiFeng Chen, Dilip Krishnan, Dina Katabi, Phillip Isola, Yonglong Tian

Our findings also suggest that scaling synthetic data can be particularly effective in scenarios such as: (1) when there is a limited supply of real images for a supervised problem (e. g., fewer than 0. 5 million images in ImageNet), (2) when the evaluation dataset diverges significantly from the training data, indicating the out-of-distribution scenario, or (3) when synthetic data is used in conjunction with real images, as demonstrated in the training of CLIP models.

Improve Supervised Representation Learning with Masked Image Modeling

no code implementations1 Dec 2023 KaiFeng Chen, Daniel Salz, Huiwen Chang, Kihyuk Sohn, Dilip Krishnan, Mojtaba Seyedhosseini

On K-Nearest-Neighbor image retrieval evaluation with ImageNet-1k, the same model outperforms the baseline by 1. 32%.

Decoder Image Retrieval +3

MatFormer: Nested Transformer for Elastic Inference

2 code implementations11 Oct 2023 Devvrit, Sneha Kudugunta, Aditya Kusupati, Tim Dettmers, KaiFeng Chen, Inderjit Dhillon, Yulia Tsvetkov, Hannaneh Hajishirzi, Sham Kakade, Ali Farhadi, Prateek Jain

Furthermore, we observe that smaller encoders extracted from a universal MatFormer-based ViT (MatViT) encoder preserve the metric-space structure for adaptive large-scale retrieval.

Decoder Language Modelling

Global Features are All You Need for Image Retrieval and Reranking

2 code implementations ICCV 2023 Shihao Shao, KaiFeng Chen, Arjun Karpur, Qinghua Cui, Andre Araujo, Bingyi Cao

Image retrieval systems conventionally use a two-stage paradigm, leveraging global features for initial retrieval and local features for reranking.

Image Retrieval Retrieval

Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models

no code implementations26 May 2023 Yunhao Ge, Jie Ren, Jiaping Zhao, KaiFeng Chen, Andrew Gallagher, Laurent Itti, Balaji Lakshminarayanan

Despite considerable effort, the problem remains significantly challenging in deep learning models due to their propensity to output over-confident predictions for OOD inputs.

Out of Distribution (OOD) Detection

Matryoshka Representation Learning

4 code implementations26 May 2022 Aditya Kusupati, Gantavya Bhatt, Aniket Rege, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, KaiFeng Chen, Sham Kakade, Prateek Jain, Ali Farhadi

The flexibility within the learned Matryoshka Representations offer: (a) up to 14x smaller embedding size for ImageNet-1K classification at the same level of accuracy; (b) up to 14x real-world speed-ups for large-scale retrieval on ImageNet-1K and 4K; and (c) up to 2% accuracy improvements for long-tail few-shot classification, all while being as robust as the original representations.

Ranked #25 on Image Classification on ObjectNet (using extra training data)

4k Image Classification +2

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