Update README.md

README.md

@@ -17,46 +17,3 @@ cloned and installed [Oscar](https://github.com/microsoft/Oscar) and
 [scene\_graph\_benchmark](https://github.com/microsoft/scene_graph_benchmark) in the directory running the notebook
 from (you can change these directories in the notebook).
 
-# Week2 Record 
-New dataset from ImageNet: 
-- 10 Classes from ImageNet-1k
-    - location: inst-01 /nobackup/ImageNet
-    - classes: n04552348 (plane), n04285008 (car/automobile), n01530575 (bird), n02123597 (cat), n02422699 (antelope), n02107574 (dog) ,n01641577 (frog)
-       , n01728572 (snake), n03095699 (ship), n03417042 (truck)
-- Tasks:
-  - generate Captions
-  - fine tune with multi-modal contrastive loss
-# Week1 Record 
-
-Q: What are desirable properties of pre-trained CLIP?
-
-- It recognizes objects (not background)! -> it's been shown that CLIP is robust across background shift
-- It associates image representations with label descriptions
-- If the true label is available, it assigns high confidence
-
-Q: Problems of pre-trained CLIP?
-
-- If the true labels are not there, it can still be overconfident
-
-Q: [detection side] Now we have text embedding, how to design a better detection score?
-
-- feature-based approach:
-    - NIPS-2021 cheating approach (assume OOD labels are known)
-    - Inner product based
-        - only using ID labels
-        - find a fixed sef of template labels?
-    - KNN
-    - Mahalanobis score
-    - improved Mahalanobis score
-        - challenge: there is a mismatch between text and img feature spaces, although they have the same dimension
-- logit-based approach: (to check if text embeddings are useful)
-    - based on pre-trained CLIP - > linear probe
-    - based on pre-trained ViT (without pre-training with text encoder) -> linear probe
-
-Q: [fine-tuning side] Now we have an ID dataset, how to make CLIP aware of ID and OOD?
-
-- baseline #1: just train with contrastive loss with ID only
-- baseline #2: add K+1 class as "OOD" and use an auxiliary pool
-    - challenge: CLIP is good at recognizing concrete objects; is it still good for abstract notions such as "OOD"?
-- baseline #3: typical OE approach: use uniform distribution as proxy for outliers
-- baseline #4: [grouping-based] similar to the MOS paper (say we have 8 big categories from ImageNet): some of them are ID (e.g. plants) and some of them are OOD (e.g. furniture). This is more abstract but more specific than “placeholder”. And during fine-tuning, we can assign ID and auxiliary outliers to those categories.