1. Ready-made DevBox:

   • Dell Alienware: at most 2 GPUs
   • newegg: 4 GPUs
   • Lambda Labs: 4 GPUs

2. Assemble: cheap, but no warranty

   • part list: most things are out of date. Tom’s hardware is a good website for comparison.

3. Nvidia

   • microarchitecture: maxwell->pascal->volta

   • DGX-systems

4. GPU cloud

Some related papers: [1][2][3][4]

Reference

1. Pun, Chi Seng, Kelin Xia, and Si Xian Lee. “Persistent-Homology-based Machine Learning and its Applications—A Survey.” arXiv preprint arXiv:1811.00252 (2018).

2. Carlsson, Gunnar, and Rickard Brüel Gabrielsson. “Topological approaches to deep learning.” arXiv preprint arXiv:1811.01122 (2018).

3. Gabrielsson, Rickard Brüel, and Gunnar Carlsson. “Exposition and interpretation of the topology of neural networks.” 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019.

4. Bergomi, Mattia G., et al. “Towards a topological–geometrical theory of group equivariant non-expansive operators for data analysis and machine learning.” Nature Machine Intelligence 1.9 (2019): 423-433.

1. Learning from Massive Noisy Labeled Data for Image Classification: hidden variable is the label noise type

2. Expectation-Maximization Attention Networks for Semantic Segmentation: hidden variable is dictionary basis

1. Do segmentation, image enhancemnet, and inpainting simultaneously [1]

2. Learning to Segment via Cut-and-Paste [2]

Reference

[1] Ostyakov, Pavel, et al. “SEIGAN: Towards Compositional Image Generation by Simultaneously Learning to Segment, Enhance, and Inpaint.” arXiv preprint arXiv:1811.07630 (2018).

[2] Remez, Tal, Jonathan Huang, and Matthew Brown. “Learning to segment via cut-and-paste.” Proceedings of the European Conference on Computer Vision (ECCV). 2018.

1. Conditioned on label vector: conditional GAN [4], CVAE-GAN [6]

2. Conditioned on a single image

   • pix2pix [1]; high-resolution pix2pix [2] (add coarse-to-fine strategy); BicycleGAN [3] (combination of cVAE-GAN and cLR-GAN)
   • DAGAN [5]

Reference

[1] Isola, Phillip, et al. “Image-to-image translation with conditional adversarial networks.” CVPR, 2017

[2] Wang, Ting-Chun, et al. “High-resolution image synthesis and semantic manipulation with conditional gans.” CVPR, 2018.

[3] Zhu, Jun-Yan, et al. “Toward multimodal image-to-image translation.” NIPS, 2017.

[4] Mirza, Mehdi, and Simon Osindero. “Conditional generative adversarial nets.” arXiv preprint arXiv:1411.1784 (2014).

[5] Antoniou, Antreas, Amos Storkey, and Harrison Edwards. “Data augmentation generative adversarial networks.” arXiv preprint arXiv:1711.04340 (2017).

[6] Bao, Jianmin, et al. “CVAE-GAN: fine-grained image generation through asymmetric training.” ICCV, 2017.

Global color mapping:

• 3D LUT: [1], [2] non-uniform LUT
• curve function: [1]
• linear transformation: [1]

Local color mapping:

• 3D LUT: [1]
• curve function: [1], DCE[2]
• linear transformation: HDRNet[1]

• image classication: CLIP [1], learnable prompt [2]

• video classification: ActionCLIP [3]

• object detection: ViLD [4], ZSD-YOLO [6]

• segmentation: [8] [9]

• visual grounding: CPT [5]

• image translation: StyleClip [7]

Reference

[1] Radford, Alec, et al. “Learning transferable visual models from natural language supervision.” arXiv preprint arXiv:2103.00020 (2021).

[2] Zhou, Kaiyang, et al. “Learning to Prompt for Vision-Language Models.” arXiv preprint arXiv:2109.01134 (2021).

[3] Wang, Mengmeng, Jiazheng Xing, and Yong Liu. “ActionCLIP: A New Paradigm for Video Action Recognition.” arXiv preprint arXiv:2109.08472 (2021).

[4] Gu, Xiuye, et al. “Zero-Shot Detection via Vision and Language Knowledge Distillation.” arXiv preprint arXiv:2104.13921 (2021).

[5] Yao, Yuan, et al. “CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models.” arXiv preprint arXiv:2109.11797 (2021).

[6] Xie, Johnathan, and Shuai Zheng. “ZSD-YOLO: Zero-Shot YOLO Detection using Vision-Language KnowledgeDistillation.” arXiv preprint arXiv:2109.12066 (2021).

[7] Patashnik, Or, et al. “Styleclip: Text-driven manipulation of stylegan imagery.” ICCV, 2021.

[8] Xu, Mengde, et al. “A simple baseline for zero-shot semantic segmentation with pre-trained vision-language model.” arXiv preprint arXiv:2112.14757 (2021).

[9] Lüddecke, Timo, and Alexander Ecker. “Image Segmentation Using Text and Image Prompts.” CVPR, 2022.

• Typical works: CapsNet [1], CapProNet [2] [code]

• The robustness of Capsule network: [3]

Reference

[1] Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules[C]//Advances in Neural Information Processing Systems. 2017: 3856-3866.

[2] Zhang L, Edraki M, Qi G J. CapProNet: Deep feature learning via orthogonal projections onto capsule subspaces[J]. arXiv preprint arXiv:1805.07621, 2018.

[3] Jindong Gu, Volker Tresp, Han Hu, “Capsule Network is Not More Robust than Convolutional Network”, CVPR 2021.

• Camouflaged Object Detection: [1]

• Camouflaged Object Segmentation [2]

Reference

1. Fan, Deng-Ping, et al. “Camouflaged object detection.” CVPR, 2020.

2. Yan, Jinnan, et al. “MirrorNet: Bio-Inspired Adversarial Attack for Camouflaged Object Segmentation.” arXiv preprint arXiv:2007.12881 (2020).

1. propagate information within each non-boundary region [1]

2. focus on unconfident boundary regions [2]

3. fuse boundary feature and image feature [3]

Reference

[1] Ding, Henghui, et al. “Boundary-aware feature propagation for scene segmentation.” ICCV, 2019.

[2] Marin, Dmitrii, et al. “Efficient segmentation: Learning downsampling near semantic boundaries.” ICCV, 2019.

[3] Takikawa, Towaki, et al. “Gated-scnn: Gated shape cnns for semantic segmentation.” ICCV, 2019.