we study how to leverage the learned representations for one-class classification.
2.We achieve strong performance on visual one-class classification benchmarks. such as .
3.While contrastive representations have achieved state-of-the-art performance on visual recognition tasks ,we argue that it could
be problematic for one-class classification.

A pictorial example is in Figure 2c, where thanks to augmented distribution, the inlier distribution may become more compact.

However, building a model that can describe the differences between the normal and abnormal only by learning the representation of normal samples
has turned out to be extremely challenging than expected.

In this section, we present the results on the publicly available GRID dataset [16]. The GRID dataset consists of videos of 33 speakers, each uttering 1000 different sentences.

we are able to considerably outperform previous methods for self-supervised and semi-supervised
learning on ImageNet.

In addition, unsupervised contrastive learning benefits from stronger data augmentation than supervised learning.

SimCLR performs on par with or better than a strong supervised baseline (Kornblith et al., 2019) on 10
out of 12 datasets

Here we lay out the protocol for our empirical studies, which
aim to understand different design choices in our framework

We observe that no single transformation suffices to learn good representations,
even though the model can almost perfectly identify the positive pairs in the contrastive task. When composing augmentations, the contrastive prediction task becomes harder, but the quality of representation improves dramatically.

We also note that ResNet-152 is only marginally better than ResNet-152, though the parameter size is almost doubled, suggesting
that the benefits of width may have plateaued

We
show that BYOL performs on par or better than the current state of the art on both transfer and
semi-supervised benchmarks.
14， We measure this by benchmarking the zero-shot transfer
performance of CLIP on over 30 existing datasets and find it can be competitive with prior task-specific supervised
models拦宣。

Our initial approach, similar to VirTex, jointly trained an
image CNN and text transformer from scratch to predict the
caption of an image.

Autonomous driving has attracted much attention over
the years but turns out to be harder than expected, probably due to the difficulty of labeled data collection for model
training.

Here we deploya simple implementation of MoCo-based MultiSiam and obtain further improvements(e.g., 0.4% mAP and 1.4% mIoU on Cityscapes in Table 1)

The dominant paradigm for training deep networks in
computer vision is by pretraining and finetuning [20, 29].
Typically, the pretraining is optimized to find a single
generic representation that is later transferred to various
downstream applications.

Three views, namely V1, V2 and V3, are used in SoCo.

The underlying assumption is that randomly
cropped and resized regions of a given image share information about the objects of
interest, which the learned representation will capture.

This assumption is mostly
satisfied in datasets such as ImageNet where there is a large, centered object, which
is highly likely to be present in random crops of the full image.

Our experiments help to narrow down scene cropping as one main cause of
the poor performance of SSL on OpenImages, rather than other differences with ImageNet, such as
object size, class distributions or image resolution.

A problem that complicates detection is the discrepancy
between an image region and its spatially corresponding
deep features.

Pre-training has also become the de-facto approach in vision-language modeling

The resulting dataset is noisy, but is two orders of magnitude larger than the Conceptual Captions dataset.

ALIGN outperforms the previous SOTA method by over 7% in most zero-shot and fine-tuned metrics in Flickr30K 舷嗡。
27.We use the name of Florence as the origin of the trail for exploring vision foundation models, as well as the birthplace of Renaissance.
28.Our motivation for model design is detailed below.
29.However, to gain fine-grained understanding of images, as required by many tasks, such as object detection, segmentation, human pose estimation, scene understanding, action recognition , visionlanguage understanding, objectlevel visual representations are highly desired.
30.In this paper, we show that phrase grounding, which is a task of identifying the fine-grained correspondence between
phrases in a sentence and objects in an image, is an effective and scalable pre-training task to learn an objectlevel。
31.We present the Pathways [1] Autoregressive Text-to-Image (Parti) model, which
generates high-fidelity photorealistic images and supports content-rich synthesis
involving complex compositions and world knowledge.
32.Generative modeling of photo-realistic videos is at the frontier of what is possible with deep learning
on currently-available hardware.
33.our architecture is\able to generate samples competitive with stateof-the-art GAN models for video generation on the BAIR Robot dataset